ICCS ABSTRACTS: Alphabetically by first author 

 Iqbal Adjali - BTexact Technologies

Modelling Third Generation Mobile Spectrum Auctions. Iqbal Adjali, David Collings and Paul Marrow

 Licences for Third Generation Mobile (3GM) spectrum have been auctioned in the UK and other countries
around the world. An understanding of the dynamics of the auction design used, and the likely strategies
which bidders might have to adopt, was of vital importance to telecom operators who wished to win a
licence in these spectrum auctions. There is also increasing interest in auctions in general, born out
of the burgeoning Internet-based auction business and the potential use of auctions to sell telecoms
bandwidth on the wholesale and even retail markets. Research into auction theory will therefore be of
increasing importance to providers of telecommunication services. The UK 3GM Auction used a structure
based on simultaneous compulsory bidding in a form of multi-unit ascending auction. The structure is
based upon the type of auction used successfully by the US Federal Communications Commission in selling
radio spectrum in the United States, but introduces a number of new features which are thought to
improve the effectiveness of the auction mechanism. A team from BT with expertise in game theory and
complex system modelling applied two techniques to aid the understanding of the UK 3GM Auction. First,
they constructed a simulation tool which allowed the modelling of entire auctions, and the analysis of
the different outcomes produced by an ensemble of auctions. In this paper results from the simulation
tool are presented and compared with the actual auction. The second analysis technique used was the
construction of a mock auction tool which could be used to facilitate practise auctions, in order to
learn bidding strategies interactively. This tool was used in practise for the auction in training the
teams involved in bidding. Here results of the mock auctions are discussed and their effectiveness in
preparing for the real auction is analysed. In the final section of the paper the likely role of
auctions in telecommunications in a wider context are discussed. 


Christof Aegerter - Vrihe Universiteit Amsterdam

Two-Dimensional Rough Surfaces: Experiments on Superconductors and Rice-Piles.

 The study of the kinetic roughening of interfaces has long been studied in e.g. imbibition systems.
These studies were however up to now restricted to a single spatial dimension. We present two new
experimental systems, where the roughening process can be studied in two spatial dimensions. In the
first case, the surface of a 2d pile of rice is studied and in the second case the magnetic fluxscape in
a type-II superconductor is investigated. Furthermore, we discuss some questions arising in the analysis
of the structure of such surfaces. 

 Nelli Ajabyan - Institute
Hydroecology&Ichthyology, NAS Armenia

Global Stability and Oscillations in the Mathematical Models of Population Interactions With Spatial
Heterogenity

 This paper presents the investigation of oscillations and nonlinear dynamics of models of ecological
systems with spatial heterogeneity. The dynamics of coupled oscillators is proved to be relevant in the
study of pattern generation of a biological system. Patterns of Hopf bifurcation, started with Turing
model, have been an active subject of research in recent years [1]. The processes of pattern generation
in biological systems exhibit universal geometric properties in one hand and a high sensitivity with
respect of external parameters on the other hand. The contrast between general geometric properties of
generated patterns and their strong dependence on the parameters of a particular process make them a
very attractive subject of theoretical and experimental research. The recognition that the coupling
network acts as a filter suggests that it is possible to alter bifurcation scenarios by using different
filters in the coupling. It is well acknowledged that the presence of symmetry in a dynamical system
will change the generic behavior of that system. This provided credence to the idea of using approximate
maps and the concepts of symmetry breaking bifurcation of chaotic attractors in physical experiments
[2]. In this paper some global bifurcation phenomena associated with networks of identical oscillators
will be reviewed. As an application global bifurcation of phase locked oscillators are applied to the
study of migratory affects in predator-prey system. The model of n systems connected with migration
flows was studied by Svirezhev Yu M.[3]The study of nonlinear phenomena has applied motivation in
providing the concept for some generations of ecological models. To obtain the models that based on more
realistic hypotheses about natural system behavior it is possible to lean up on well-known mathematical
concepts. A trophic chain with nonlinear functions describing prey-predator interaction is one of the
examples of such generalization. The application of the ecological stability concept allows gaining
model explanation of some observations and effects that had not interpretation in the frame of existing
mathematical theory. Literature cited1. Ashwin P., King C.P., W. Swift. Three identical oscillators with
symmetric coupling. Nonlinearity, 3,1990, p581-601. Priented in UK.2. J.C. Alexander G Auchmuty Global
Bifurcations of Phase-Locked oscillators . Archive for rational meachanics and Analysis Springer_Verl.
Berlin, v. 93, N 3, 1988, p 253-2703. Svirezhev Yu. M. Nonlinear waves dissipative structures and
catastrophes in ecology. Moscow, 1987. 

 Shah Alam - Shahjalal University of
Science and Technology

Algebra of Mixded Number

 Mixed number is the sum of a scalar and and vector like quaternion but the algebra of mixed number is
different from that of quaternion. In this paper the details algebra (math tools) of mixed number is
explained. 

 Eric Allison - Pratt Institute

Self Organization in Cities: Case Studies in Neighborhood Historic Preservation

 While the theory of cities as self organizing entities is documented and acknowledged in both academic
and popular writing (Jacobs, 1993; Johnson 2001; Portugali, 2000; Bar-Yam, 1977, nd), there are
relatively few case studies documenting actual examples of emergent self organization in cities. This
paper describes the self organization which took place in New York City to effect the designation of
multiple neighborhoods as historic districts in the last third of the twentieth century. This unexpected
emergent behavior is continuing and spreading via formal and informal networks to other cities. In 1965,
the Mayor of New York signed into law a bill creating a Landmarks Preservation Commission charged with
designating and protecting historic structures in New York City. The newly-created Landmark Preservation
Commission could designate both individual buildings and historic districts. The New York City law was
the first of its kind in the United States. The Landmarks Preservation Commission, after public
hearings, could designate a building or district administratively. The designations took place
immediately. They were then submitted to the local legislature to preserve due process. The legislative
body originally the Board of Estimate, now the City Council were required to give a yes or no vote
within sixty days. The intent was that the Commission, staffed by experts, would survey the city, select
appropriate buildings, hold hearings, and designate those found worthy, via a top-down hierarchical
process. At the hearing for the bill creating the Commission, the future Executive Director of the
Commission who had helped write the bill testified that he anticipated eventually designating
approximately 1,000 individual landmarks and two or three historic districts. These initial conditions
led to a very different result than anticipated. Thirty-seven years later, New York City has some 1,300
individual landmarks not far off the original estimate but it also has over seventy historic districts,
encompassing more than 22,000 buildings. Among other factors, this unanticipated result emerged from the
responses of individual agents (city dwellers) to changing environment conditions, including:* changes
in the funding and personnel time and emotional energy available to support the goals;* changes in the
goals, themselves, as a result of the need to solve a host of complex social problems; * increased
competition for desirable, affordable places to live; and* immersion in the phase change occurring
throughout human civilization from hierarchical to hybrid and networked control structures (Bar-Yam,
nd). Space permitting, I will develop four case studies. Three cases will describe different instances
of self organizing neighborhoods and citizens (the Fort Greene Historic District in Brooklyn and the
Tribeca Historic District and the Ladies Mile Historic District both in Manhattan). The fourth will
document how New York City's Historic Districts Council created to garner support in the city's historic
districts for increasing the budget of the Landmarks Preservation Commission re-organized itself as a
network node among the various independent neighborhood groups. 

 Madhur Anand
- Laurentian University

The Evolution of Complexity in Natural and Reconstructed Ecological Assemblages. Madhur Anand, Ke-Ming
Ma, Brian Tucker and Rachelle Desrochers

 We study the complexity of ecological assemblages that have been severely damaged by man-made
perturbation (mainly air pollution) and ask the question: how is complexity affected and how well can we
alter recovery trajectories by intervention? We use data from both field and laboratory experiments in
Sudbury, Ontario that documented plant and microbial assemblage dynamics after varying degrees of
perturbation and rehabilitation in 2001. Assemblage-level patterns are modelled using two approaches.
The first attempts to summarize taxa-interactions either using information-theoretical measures or a
reduced multivariate space. The second attempts to fit a stationary Markov model to assemblage dynamics.
Assemblage-level dynamics was effectively summarized using Principal Components Analysis (> 80%
variation captured in first two axes); however spatiotemporal structure was not very well captured. The
Markov models provide an excellent fit to observed dynamics (p<0.001). The results show that in all
treatments the microbial assemblages reach equilibrium quickly but have different recovery pathways
under different treatments and that initial state matters. Information theoretical measures reveal that
the assemblage dynamics cannot be attributed to any single or dominant taxon but is rather an emergent
property of the system. 

 James J. Anderson - Center for Computational
Biology and Bioinformatics NIGMS

NIGMS Funding Opportunities for Complex Biomedical Systems Research and Training

 The National Institute of General Medical Sciences (NIGMS: a component of the U.S. National Institutes
of Health) announces a new Center for Bioinformatics and Computational Biology. This Center will promote
advances in cross-disciplinary research, education, and training involving quantitative approaches to
complex biological and biomedical problems, and allied bioinformatics. NIGMS has issued a number of
program announcements (PA) and requests for applications (RFA) that will provide support for these
areas. The suite of initiatives have as their foci: 1. The understanding of system principles and
dynamics in processes involving large numbers of interacting components, at all levels of
biologicalorganization within the scope of the NIGMS mission; 2. The development of analytical
methodologies to discover the genetic architecture of complex genetic traits; 3. The study of the
evolutionary dynamics of pathogens and their hosts with their environments; 4. The development of
enabling technologies useful for the study of metabolic processes and metabolic engineering; 5. The
development of basic mathematical concepts and algorithms that have the potential for significantly
advancing the state of the art inbiomedical research. The initiatives comprise mechanisms to fund
research projects (traditional research project grants (R01) and program project grants (P01)), to fund
establishment of integrative research efforts ("glue grants," R24), to fund extensive programs of
research related activities (Center grants(P50)), to provide support for short courses and workshops
(R25 education grants) for both biologists and non-biologists, and to provide trainingat both pre- and
postdoctoral level (T32 and T33 Training Grants). Detailed information on these programs will be
available, and can also be found at the following URL:http://www.nih.gov/nigms/funding. 

 Pierpaolo Andriani - University of Durham Business School

 The work presented in this paper aims at providing an empirical validation to some aspects of
Kauffman's laws of complex systems (Investigations, 2000). "...On a coevolutionary timescale, coevolving
autonomous agents as a community attain a self-organised critical state by tuning landscape structure
and couplings between landscapes, yielding a global power law distribution of extinction and speciation
events and a power law distribution of species lifetimes" and "...As an average trend, biospheres and
the universe create novelty and diversity as fast as they can manage to do so without destroying the
accumulated propagating organisation that is the basis and nexus from which further novelty is
discovered and incorporated into the propagating organisation" The work examines Italian industrial
clusters (ICs). ICs are geographic concentrations of interconnected companies and institutions in a
particular field, encompassing an array of linked industries and other entities important to
competition. Individual firms tend to be flexibly specialised in a particular production phase, through
relationships of both competition and co-operation. The work takes as unit of analysis self-contained
socio-economic areas known as travel-to-work areas (self-contained areas of home-work commuting),
classified in a range spanning from ICs to low industrial activity areas. A statistical analysis based
on complexity theory frameworks shows: 1. ICs are closer to self organised critical (SOC) systems than
other types of geographic agglomerates. SOC is explored in terms of nodal properties (rank-size rule) 2.
ICs seem to explore the space of diversity within the envelope of their extended value chain maintaining
the 'propagating organisation' at a higher rate than non ICs. 3. The product of internal diversity
(industrial species) times internal connectivity is higher for a community of autonomous agents than for
an aggregate. This work defines diversity as composed of three elements: variety (roughly number of
categories necessary to classify species), disparity (distance between categories) and balance
(apportionment of species per category) and applies distance metrics ideas to measure diversity. The
propagating organisation is measured by a proxy, that is the internal connectivity of the system. This
work arrives at the following conclusions: 1. The frameworks of complexity theory used in this work,
namely self-organised criticality and exploration of diversity at the subcritical-supracritical
boundary, are useful to interpret socio-economic structural and dynamical properties of geographic
agglomerations of firms. 2. SOC and diversity can be used as tools to discriminate between (industrial)
aggregate and system of autonomous agents, thereby suggesting a phase transition between the two. 3. The
structure of interdependence among agents (organisations) in a local agglomerate is related to the
closeness to a power law (SOC behaviour) of the local aggregate's structural and behavioural properties,
thereby confirming Kauffman's intuition. 4. The preliminary exploration of the structural properties of
Italian industrial agglomerations confirms some aspects of Kauffman's fourth laws, specifically the fact
that communities of autonomous agents explore the phase space of diversity (adjacent possible) at a rate
superior to that of aggregates of autonomous agents. 

 Takeshi Arai - Tokyo
University of Science

A CA Based Two-Stage Model of Land Use Dynamics in Urban Fringe Area. 

 Arturo
H. Ariño - University of Navarra

Optimal Sampling For Complexity In Soil Ecosystems

 Soil ecosystems are inherently complex: space, time and biological diversity interact giving way to
emergence of dynamic, complex features such as distribution patterns, abundance profiles, nutrient
paths, etc. Specifically, sampling for soil diversity is fraught with problems that arise form the very
different spatial scales that involve biological populations' aggregates and subpopulations. Typical
sampling techniques tend either to be ineffective for complexity assessment (i.e. too small to capture a
representative subset of most populations and their distributions) or overshot their target with very
large samples that can be cost-ineffective. Optimized sampling techniques that use the species-area
curves may be inadequate for the purpose of measuring diversity, as they typically focus on the species
accumulation rather than on the measurement of structure. Also, species-area curves are sensitive to the
accumulation mechanism: the order in which subsamples accumulate matters. We propose an algorithmic
method that tries to capture enough data for a cost-effective diversity (complexity) assessment while
statistically ensuring consistency. Tests have been done with actual, species-level soil mesofauna fauna
data. A C program implements the algorithm. 

 Rajkumar Arumugam -
Department of Electrical & Computer Engineering and Computer Science University of Cincinnati

Intelligent Broadcast in Random Large-Scale Sensor Networks Rajkumar Arumugam, Vinod Subramanian, Ali A.
Minai

 With advances in miniaturization, wireless communication, and the theory of self-organizing systems, it
has become possible to consider scenarios where a very large number of networkable sensors are deployed
randomly over an extended environment and organize themselves into a network. Such networks --- which we
term large-scale sensor networks (LSSN's) --- can be useful in many situations, including military
surveillance, environmental monitoring, disaster relief, etc. The idea is that, by deploying a LSSN, an
extended environment can be rendered observable for an external user (e.g., amonitoring station) or for
users within the system (e.g., persons walking around with palm-sized devices). Unlike custom-designed
networks, these randomly deployed networks need no pre-design and configure themselves through a process
of self-organization. The sensor nodes themselves are typically anonymous, and information is addressed
by location or attribute rather than by node ID. This approach provides several advantages, including:
1) Scalability; 2) Robustness; 3) Flexibility; 4) Expandability; and5) Versatility. Indeed, this
abstraction is implicit in such ideas as smart paint, smart dust, and smart matter. The purpose of our
research is to explore how a system comprising a very large number of randomly distributed nodes can
organize itself to communicate information between designated geographical locations. To keep the system
realistic, we assume that each node has only limited reliability, energy resources, wireless
communication capabilities, and computational capacity. Thus, direct long-range communication between
nodes is not possible, and most messaging involves a large number of ``hops''between neighboring nodes.
In particular, we are interested in obtaining reliable communication at the system level from simple,
unreliable nodes. Wireless networks that operate without fixed infrastructure are called ad-hoc
networks, and are a very active focus of research by the wireless community. However, most of the work
focuses on networks with tens or hundreds of nodes, where most message paths are only a few hops long.
All data messages in such a system are unicast, i.e., they are between specific pairs of nodes. There
are two major formulations for this. In some message routing algorithms, a path discovery process is
used to first find a route between the source and destination nodes (or locations), and the message is
then sent along this path. This is clearly a top-down approach with limited scalability. Other routing
protocols use next-hop routing, where each node, knowing the destination of an incoming message, only
determines the next node to forward the message to. These protocols scale much better, but at the cost
of maintaining and updating extensive amounts of information about network topology. This can be
expensive in terms of energy, and can often lead to problems if the individual nodes are unreliable,
causing broken links and lost messages. From a complex systems viewpoint, the problem with unicast-based
next-hop methods is that they do not exploit the inherent parallellism of the system to achieve
robustness. This is the issue we consider in our research. Rather than using directed unicast between
nodes, we study the possibilities of broadcast. In the simplest case, this corresponds to flooding,
where every message received by a non-destination node is ``flooded'' to all the node's neighbors. While
this is a simple apprach, it is extremely wasteful of bandwidth and creates a lot of collisions --- the
simultaneous use of the wireless channel by multiple messages, all of which are lost as a consequence.
To overcome the problems of flooding while retaining its inherent parallellism, we explore the method of
intelligent broadcast. In this approach, each node receiving a message decides whether to re-broadcast
it to all its neighbors or to ignore it. Note that the decision does not involve selecting which
neighbor the message is forwarded to, but only whether to forward the message. The latter is a much
simpler decision, and can be made on the basis of the information carried by the message incombination
with that available within the potential forwarding node. This approach leads to a self-organized
communication process where local decisions by the nodes produce global availability of information. In
the paper, we present a well-developed paradigm for random LSSN's, including a model for the nodes and
viable broadcast-based protocols for channel access and network organization. We evaluate the
performance of the network in the case of simple flooding, and then study the effect of a simple
decision heuristic that allows nodes to limit messagere-broadcast based on how many hops the message has
already travelled. We show that this heuristic leads to a dramatic improvement in performance, making
the broadcast-based system a viable --- and more robust --- alternative to more complicated systems
under some conditions. We also characterize how network parameters such as size, node density, messaging
rate and node reliability affect the performance of the heuristic. 

 Edward A Bach
- Boston University

SIMP/STEP: A Platform for Fine-Grained Lattice Computing.

 SIMP/STEP, is a platform for fine-grained lattice computing such as that of cellular automata (CA),
lattice gases/partitioned CA (LG/PCA), and pixel-level image processing (IP) operators. The SIMP
programming environment targets the needs of complexity experimenters, physical modelers, and IP
programmers who want to quickly write efficient and readable massively-parallel programs without
worrying about the underlying implementation. The STEP abstract machine interface is a set of
fine-grained lattice computing primitives into which SIMP programs are compiled. Through some specific
examples, we demonstrate how to program CA and LG/PCA in SIMP. We also describe a few complex-system
modeling approaches such as using LG/PCA to make an invertible global dynamics out of an invertible
local dynamics and using a fine-grained, discrete, microscopic dynamics to synthesize a system that
obeys some macroscopic continuous differential equation. Finally, we highlight some implementation and
performance aspects of STEP. In particular, we discuss PC-STEP, a software STEP kernel and a STEP
hardware accelerator design styled after the CAM-8 of Toffoli and Margolus. 


Alan Baker - Xavier University

Philosophy and Complexity

 Complexity theory has largely ignored – and been ignored by mainstream philosophy. This is
unfortunate and also surprising, for both fields are by nature wide-ranging in their score and
interdisciplinary in their impact. My aim in this paper is to sketch some possible paths for fruitful
interaction between these two fields. I shall argue that each has conceptual tools which are of
potential benefit to the other. For the purposes of this paper I shall concentrate primarily on the
philosophy of science, since it is here that the most immediate connections with complexity theory are
to be found. Contemporary analytic philosophy has largely abandoned to the natural sciences the detailed
work of explaining and predicting physical phenomena (a task which used to fall under the heading of
natural philosophy). This is especially true for physical phenomena that are complex. Identifying,
experimenting upon, analyzing, and modeling such phenomena is most effectively accomplished by those
with expertise in the broad range of mathematical, scientific, and social scientific fields which fall
under the broad heading of complexity theory or complexity science. Philosophy, by contrast, is a
meta-discipline, focusing not so much on the nature of the world itself as on our various ways of
engaging with the world. Philosophical expertise may play a valuable role, especially with respect to
the following two topics; (i) Complexity theory, considered as an object of study in its own right. Is
complexity theory methodologically distinct from traditional science? Is it revolutionary in the sense
articulated by Thomas Kuhn? (ii) Complexity, considered as an abstract concept. Is it a unitary concept
or a cluster of related but distinct concepts? Can complexity be adequately defined? How does it relate
to other notions such as disorder, randomness, etc.? My particular focus in what follows concerns the
methodology of theory choice in science. Philosophers of science have long been interested in the
criteria by which scientists evaluate and choose between competing theories. There are many factors
influencing these decisions which have little if anything to do with the content of the theory itself,
factors such as the reputation of the theory's authors, the place and manner of its publication, and the
likelihood of future funding. Other factors are internal to the theory itself. One important such factor
is simplicity. The preference for simple theories is often referred to as Occam's Razor. An area of
ongoing debate among philosophers of science concerns how to define simplicity. There has been a
parallel debate going on among complexity theorists concerning how to define a satisfactory notion of
complexity. There has been little, if any, feedback between these two debates, yet each has the
potential to inform the other. Below I list some of the main points I shall discuss in the body of the
paper; (a) Philosophers tend to analyze simplicity as a property of theories or hypotheses, whereas
complexity theorists typically analyze complexity as a property of phenomena. This links back to my
earlier point that philosophy is by nature a meta-discipline. (b) Philosophers distinguish between
syntactic simplicity (or elegance), which is a measure of a theory's conciseness, and ontological
simplicity (or parsimony), which is a measure of how many things, or kinds of things, a theory claims
exist. Parallel concepts of syntactic and ontological complexity may be defined. (c) Philosophers have
focused on defining comparative but non-quantitative notions of simplicity. By contrast, complexity
theorists have generally aimed to define quantitative measures of complexity. (d) What, if any, rational
justification can be provided for Occam's Razor? Why should we value simple theories, or expect them to
be more likely to be true, especially in a world apparently full of chaotic and complex phenomena? Ought
Occam's Razor to be a methodological principle of complexity theory? One of the goals of complexity
theory has been to identify general features common to complex phenomena in different contexts such as
the human brain, patterns of earthquakes, or the stock market and to identify patterns and regularities
they have in common. This is one sort of higher-level simplicity. 

 Ariel
Balter - Indiana University

Levy Flights in Climate Data

 Inovations in high frequency (> 1/day) climate variables appear to be Levy Stable random variables.
Many climate variables show strongly Gaussian behavior (such as directional wind) and many (such as
temperature) do not. However, the inovations (i.e. differences) are strongly non-Gaussian. Instead, they
can be fit extremely well byLevy Stable distributions. We have examined this phenomenon for a large
number of climate variables representing a wide range of years, geographies, climatologies and sampling
frequencies. The effect appears highly universal. We believe this effect emerges from nonlinearities
which can be modeled as multiplicative noise. By establishing the ubiquitous manifestation of this
effect we hope to provide a tool for climate modelling. Most stochastic climate models use white
Gaussian noise for inovations. Switching to Levy Stable noise will undoubtably improve the usefulness of
these models. 

 Ernest Barany - New Mexico Tech

Dynamics of Ethernet Protocol

 The critical output bandwidth needed to ensure that a CSMA/CD based LAN does not accumulate packets
unboundedly in its queues is computed. This is an emergent property of the network that follows from
decentralized microscopic laws. The result follows from the stationary distribution property of ergodic
Markov chains 

 Joana Barros - Centre for Advanced Spatial Analysis -
University College London

City of Slums: Self-Organisation Across Scales. Joana Barros and Fabiano Sobreira

 Third World cities are known for their inherent chaotic and discontinuous spatial patterns and rapid
and unorganised development process. Due to the very same characteristics, in the present paper, these
cities are seen as excellent objects for the study of complex systems. We argue that the morphological
structure of these cities can be analysed by the interplay of two different urban processes across
scales: the local process of formation of inner-city squatter settlements and the global process of
"peripherization" (typical growth process of Third World cities). The basic aim of this paper is to
analyse the interrelationship between these two processes. This issue is explored through
'City-of-slums', an agent-based model that focuses on the process of consolidation of inner-city
squatter settlements within a peripherization process. The paper presents briefly two previous studies
on these topics where the dynamics of these two urban processes are examined as two isolated complex
systems through heuristic agent-based models and their morphologies are discussed. We then combine
aspects of these two dynamics to compose City-of-slums, in an attempt to discuss the role of
self-organisation in the spatial dynamics of Third World cities. It is suggested that the resulting
urban morphology, although related to distinct scales, present similar degree of fragmentation (fractal
pattern). Preliminary observations also suggest that these complex processes are involved in a spatial
logic in which resistance is the cause, consolidation is the process and fragmentation is the result. 

 Jacob Beal - MIT AI Lab

Themes: Emergence, Self-Organization; System Categories: Psychological, Engineered

In a distributed model of intelligence, peer components need to communicate with one another. I present
a system which enables two agents connected by a thick twisted bundle of wires to bootstrap a simple
communication system from observations of a shared environment. The agents learn a large vocabulary of
symbols, as well as inflections on those symbols which allow thematic role-frames to be transmitted.
Language acquisition time is rapid and linear in the number of symbols and inflections. The final
communication system is robust and performance degrades gradually in the face of problems. 

 Pierre Sener - Iridia

The Connections Between the Frustrated Chaos and the Intermittency Chaos in Small Hopfield Network.
Hugues Bersini and Pierre Sener

 Frustrated chaos is a dynamical regime which appears in a network when the global structure is such
that local connectivity patterns responsible for stable oscillatory behaviours are intertwined, leading
to mutually competing attractors and unpredictable itinerancy among brief appearance of these
attractors. In this paper, through a detailed study of the bifurcation diagram given for some connection
weights, we will show that this frustrated chaos belongs to the family of intermittency type of chaos.
The transition to chaos is a critical one, and all along the bifurcation diagram, in any chaotic window,
the duration of the intermittent cycles, between two chaotic bursts, grows as an invert ratio of the
connection weight. We will more specifically show that anywhere in the bifurcation diagram, a chaotic
window always lies between two oscillatory regimes, and that the resulting chaos is a merging of, among
others, the cycles at both ends. Since in our study, the bifurcation diagram concerns the same
connection weights responsible for the learning mechanism of the Hopfield network, we will discuss the
relations existing between bifurcation, learning and control of chaos. We will show that, in some cases,
the addition of a slower Hebbian learning mechanism onto the Hopfield networks makes the resulting
global dynamics to drive the network into a stable oscillatory regime, through a succession of
intermittent and quasiperiodic regimes. 

 Howard A. Blair - Syracuse University

Unifying Discrete and Continuous Dynamical Systems

 We analyze the structure of dynamical systems (DS) in such a way as to reveal a structural parameter
that differentiates among the kinds of DSs that frequently arise. This structural parameter is present
in each of four components of nearly all DSs, including those that arise in the context of quantum
computing. The four components are (1) the computation space, (2) the temporal structure, (3) the local
state space, and (4) the differential state space. Each of these four components can be varied
independently, and can be chosen to have either a discrete structure in the sense of data-structures, or
a continuous structure in a strong topological sense. There are a variety of product operations that
enable the crafting of hybrid structures. The desired structure for each of the DS's components is
obtained in a principled fashion by specifying the structural parameter: a field of filter families that
satisfy certain closure properties to yield a so-called convergence space. The field of filter families
is to a convergence space as a topology is to a topological space. The notion of a convergence space is
stronger than that of a topological space. The virtue of the convergence space notion is that directed
graphs are convergence spaces, and continuity specializes in the case of directed graphs to graph
homomorphisms.The convergence space components of the DS can be built out of limits of directed graphs.
In particular this provides a natural way to combine discrete data-structures with topological spaces.
The trajectories of familiar DSs are the continuous solutions of constraints on the convergence space
components of those systems. An ordinary differential equation in several variables, or in countably
infinitely many variables, as in a Fermi-Pasta-Ulam continuous-valued cellular automaton, is a DS that
fits cleanly into our analysis, once it is realized that the phase space is not the computation space,
rather the phase space is the (implied global) state space. The computation space is the underlying
cellular automaton structure. One novel aspect is that various approximating solutions, as would be
given by Euler and Runge-Kutta methods, are obtained by altering the field of filter families on the
temporal structure. The utility of the convergence space analysis provides for efficiently describing
combinations of, for example, cellular automata in which the state of a cell C evolves in discrete time
steps while cells in the neighborhood of C evolves differentially in continuous time in a manner
dependent in part on the evolution of the state of C. In the end, the distinction between, say, an ODE
and a Turing machine, comes down to the field of filter families on the four components of the systems.
REFERENCES 1. Fermi, E., J. Pasta and S. Ulam, Studies of Nonlinear Problems, S. Ulam, Sets, Numbers and
Universes, MIT Press: Cambridge, 1974, pp. 491-501 2. Heckmann, R. A Non-topological View of dcpo's as
Convergence Spaces. First Irish Conference on the Mathematical Foundations of Computer Science and
Information Technology (MFCSIT 2000) July, 2000. 

 Janine Bolliger - Swiss
Federal Research Institute

A Case Study for Self-Organized Criticality in Landscape Ecology Janine Bolliger and Julien C. Sprott

 In ecology, the phenomenon of self-organized criticality may provide a powerful approach to complete
current theoretical frameworks (e.g., metapopulation theory) with a profound understanding of how
ecological feedback, interaction, and historical coincidence act together so that biotic units co-occur
at their present locations. This study investigates the self-organized critical state and the complexity
of the historical landscape of southern Wisconsin (60,000 km2). The landscape was classified into 27
discrete forest types using statistical cluster analysis. The data for classification was derived from
the United States General Land Office Surveys that were conducted during the 19th century prior to
Euro-American settlement. We applied a two-dimensional cellular automaton model with a single adjustable
parameter. The model evolves by replacing a cell that dies out at random times by a cell chosen randomly
within a circular radius r (neighborhood), where r takes values between 1 (local) and 10 units
(regional). Cluster probability measures the degree of organization. Good agreement is found when
comparing the simulated to the observed landscape using fractal dimension (spatial), fluctuations in
cluster probability (temporal), and algorithmic complexity (interaction characteristics). All results
are robust to a variety of perturbations. In our example, the self-organized state is scale-invariant in
both time and space and depends on the neighborhood size chosen for the model runs. Small neighborhoods
(r = 1 or 4 cells), representing low connectivity across landscapes, over-organize. Adjacent cells are
likely to exhibit similar properties and are thus likelier to organize, however, too small fractions of
the overall forest-type diversity are accounted for. Large neighborhoods (r = 10 or 314 cells)
representing high connection among the forest types do not organize, indicating that the likelihood of
cells interacting with cells exhibiting similar properties in large neighborhoods are rare. Intermediate
neighborhood sizes (r = 3 or 28 cells) representing intermediate levels of connectivity in forests,
where many local, but some longer distance interactions occur, give rise to self-organization to the
level of the observed forest landscape. Such ‘small-world’ phenomena studied by Watts and
Strogatz (1998) have been observed for many systems that typically range somewhere between regular and
random. We view the self-organized critical state as a measure of connectivity of the forest landscape,
since the functional significance of scale invariance is, among others, a description of how system
elements interact across the system. With this example of self-organized criticality, we show that
simple models may suffice to replicate the forest landscapes originating from complex spatial and
temporal interactions. 

 Eric Bonabeau - Icosystem Corporation

Co-Evolving Business Models

 Trying to predict the future structure of an industry is a key ingredient when it comes to defining a
company's strategy. Scenario planning is a popular method to aggregate the knowledge of industry
specialists into possible scenarios using special brainstorming sessions. Another route consists of
putting that knowledge into a model of the industry and let the industrys players co-evolve their
business strategies or business models. Existing industry knowledge is first transformed into relevant
strategic building blocks (value proposition components, operational components, revenue components)
that serve as the basic units for evolutionary recombination. The initial state of the simulation
reflects the current industry structure with respect to both business models and market shares. The
weakest players disappear and are replaced by new players that borrow building blocks from the strongest
players. After a number of generations, the industry may or may not converge toward a stationary
structure. This approach has been applied the evolution of the Internet Service Provider (ISP) industry.
In this context, a number of runs showed the emergence and later disappearance of the free ISP business
model, reflecting the actual dynamics of the ISP industry in Europe. In order to obtain reliable results
regarding the industry's stationary structure, one thousand co-evolutionary simulations were run for 500
generations. For each simulation, the endpoint population was analysed to try to answer the following
questions: are there stationary industry states, stationary business models, what are the winning
business models, what are the most likely endpoints for the industry? Surprisingly, in most runs the ISP
industry converged to a monochromatic industry structure, that is, one where all business models are
similar. Furthermore, 60% of the runs converged toward the same color, that is, the same business model,
suggesting that the industry has a very robust evolutionary attractor. Many more statistical
measurements can be extracted out of the simulations, allowing us to understand how and why the industry
converged toward a particular structure. This outcome can then be used to define an ISP's strategy. 

 Jose M. Borreguero - Center For Polymer Studies, Boston University

Fluctuation Analysis in the Transition State Ensemble of the SH3 Domain

 We perform a detailed analysis of the thermodynamics and folding kinetics of the SH3 domain fold with
discrete molecular dynamic simulations. We propose a protein model that reproduces the cooperative
folding transition experimentally observed in globular proteins. We use our model to study the
transition state ensemble (TSE) of SH3 fold proteins --- specifically, we study a set of unstable
conformations that fold to the protein native state with a probability close to 1/2. We analyze the
participation of each secondary structure element in the formation of the TSE and we find good agreement
with xperimental results of Src SH3 domain and alpha-Spectrin SH3 domain proteins. We also identify the
folding nucleus of the SH3 --- a set of specific amino acid contacts that determine whether a
conformation belonging to the TSE will fold. We predict that a set of contacts between the secondary
structure elements RT--loop and distal hairpinare the critical folding nucleus of the SH3 fold, and we
propose a hypothesis that explains this result. 

 Paul Box - Utah State University

A Computational Environment for Studying Human-Environment Interaction in Bering Sea Fisheries

 The southern Bering Sea is considered to be one of the most important fisheries in the world. Salmon
populations and salmon catches have been fluctuating in recent years, precipitating policy initiatives
that often do not consider long-term dynamics of populations, indigenous knowledge of use of the
population, nor importance of theresource to viability of local communities. There is archaeological
evidence that suggests that there have been massive fluctuations in salmon populations over the last
5000 years, and catch data show great changes in abundance over the last 150 years. The relationship
between salmon populations and the human communities that depend on them seems to be a complex adaptive
system, with many non-linear interactions between the human and salmon populations, and the environment
in which they live (including lake and river dynamics, climate, predator populations, etc.). An
agent-based computational framework is described here that specifies properties of salmon andhuman
populations, together with coupled limnological, food-web, and climate models that affect salmon
populations. 

 Raymond Trevor Bradley

 Using the concepts of energy and information, and the principles of energy conservation, holographic
organization, self-organization, and cooperation, I have collaborated with neuropsychologist, Dr. Karl
Pribram, to build a theory of communication that accounts for the endogenous processes by which stable
organization emerges in bounded social systems ("Communication and Stability in Social Collectives," R.
T. Bradley and K. H. Pribram, J. of Social and Evolutionary Systems, Vol. 21 (1): 29-81, 1998). In other
joint work, Dr. Pribram and I have mapped empirical commonalities in the neural organization in brains,
the organization of interaction in psychosocial development, and the organization of collaborative
action in social collectives (K. H. Pribram and R. T. Bradley, "The Brain the Me and the I," in
Self-Awareness: It Naure and Development, M. Ferrari and R. Sternberg (eds), Chapter Ten, pp. 273-307,
New York, The Guilford Press, 1998. 

 Alfred Brandstein - US Marine Corps

The Role of Analysis in the Brave New World

 Recent advances in science, especially computer science, have made dramatic changes in our approach to
the role of analysis. Our faith in the capabilities of modeling to support decision making has been
severely shaken. In the complex world of non-linearities and co-evolution, we are struggling to find the
proper role for modeling and analysis. The approach we are taking to define this role is discussed. 

 Michael Bretz - Dept. of Physics, Univ. of Michigan

Emergent Probability Lonergan's Genetic Model of Knowledge Growth, Development and Decline.

 A little known but intriguing heuristic model of knowledge growth and structural change was conceived
many decades ago[1]. In his treatise, Lonergan successfully disentangled the dynamic elements
surrounding the scientific intellectual process and modeled how explanatory knowledge is generated. He
extended this model to knowledge growth itself and to the dynamics underlying all development - be it
chemical, evolutionary, historical, environmental, economical, psychological, organizational or ethical.
Lonergan characterized generic growth as the successive appearance of conditioned Recurrent Schemes(RS),
each of which come into existence probabilistically once all required prior conditions (selected earlier
schemes) are in place. When formed, a new dynamic recurrent scheme becomes locked into long term
stability (some examples of RS networks are resource cycles, motor skills and habits). He envisioned the
overall concrete growth process of recurrent schemes to be highly dynamic, convoluted, non-linear and
genetic in form, so appropriately designated it "Emergent Probability" (EP). Although developed
qualitatively, EP constitutes a complex dynamic system that is ripe for computer exploration. In this
talk I will present first results from a MATLAB toy model for state space growth and evolution of EP as
simulated by a scale-free, directed growing network (nodes as RS?s, links as conditions). The appearance
of RS clusters and their interplay with each other, competition for scare resources, and dependence of
clusters on the underlying ecological situation will be emphasized. Aspects of EP appear to have been
reinvented as key elements in present day hypercycle, neuronal group and bioinformatics models, making
EP a potential vantage point for unification between, and fertilization among, the disparate
calculational approaches and interdisciplinary fields (as mentioned above). Lonergan?s stated global EP
features extend beyond the properties usually attributed to complex genetic systems, so central
questions must be addressed in the further study of Emergent Probability. 1.Insight, A Study of Human
Understanding by Bernard J. F. Lonergan (Longman, Greens & Co., London, 1957; Collected Works(3), edit.
F.E.Crowe and R.M. Doran, Toronto Press, 1992) 

 Colby Brown - Sociology,
Wesleyan University

A Graph-Dynamical Model of Transportation Development

 Charles Horton Cooley described conversation and transportation as subsets of the larger social
phenomenon of general communication. (Cooley, 1894) Cooley's classification system seems particularly
useful when Shannon's information theoretic-concept of uncertainty and the institutional economic
concept of transactions costs are compared. (Shannon, 1949, and perhaps Pitelis, 1993) Transportation
and social networks also share the potential application of methodologies based in graph theory. Noting
these precedents, we suggest that transportation development can be analyzed as an iterative graph
dynamical process in which changes in a society's structural networks effect changes in that society's
transportation infrastructure, effecting changes in social structure, etc. Within this model, the
American urban expressway development process of the mid-20th century presents an interesting puzzle. If
the social networks most relevant to interstate highway development were locally limited to the urban
areas in which development occurred, then the well-documented destructive effects of development appear
paradoxical. Theories of power or asymmetrical information might explain this state of affairs, yet, it
is more likely that the pattern of development of urban expressways depended heavily upon linkages to
dispersed (state- and national-level) institutions and actors. In that case the behavior of either
"articulation points" or "articulation groups," (i.e. groups of people who, if removed, would leave a
network cut into two or more disconnected cliques), would have been crucial to the exact pattern of
development experienced in a given locality. We examine mid-century changes in the structure of urban
social networks in America, socio-economic phenomena governing transportation development through that
time period, and construct a multi-layer graph dynamical model of transportation development based on
historical and statistical evidence. In the process, we consider the emergence of what Manuel Castells
(1983) has called "information age" urban social networks; a self-organized critical phase transition
from locally-based communications to more dispersed, though not necessarily less communal, relations. 

 Mark Burgin - UCLA

Levels of System Function Description: From Algorithm to Program to Technology

One of the most important problems of system theory is to give an efficient model for an adequate
description of system functioning. Dynamic representations become more and more important. There are
three main types of such descriptions: a contracted description as transitions of system states, e.g., a
trajectory in a state space, an extended description as the structure of operations performed by the
system, e.g., an algorithm, and a full description as a process. An extended representation of system
functioning combines advantages of both contracted and full representations. Like a contracted
representation, an extended representation is enough tractable and compact, while like a full
representation an extended representation gives a sufficiently detailed description of the process. As a
result, an extended representation eliminates scarcity of information in a contracted representation and
abundance of information in a full representation. In each type of descriptions of system functioning,
there are several levels. An extended description has three levels: algorithms/procedures, programs, and
technologies. An algorithm/procedure gives purely structural description of a process. A program gives
an internal with respect to the system under consideration description of a process. A technology gives
an external with respect to the system under consideration description of a process. Such general
understanding shows that programs are related not only to computers, while technology is used not only
in industry. It is possible to consider programs and technologies for an arbitrary system. Companies
have programs for their development. Physicists develop technologies for physical experiments.
Politicians and political scientists discuss political technologies and so on. Within this approach any
program is an algorithm or a procedure, while any technology is a program. However, it is necessary to
remark that all these concepts are relative and depend on the system under consideration: what is an
algorithm for one system may be a program or even a technology for another system. There is a developed
theory of algorithms. There are some components of the theory of programs, for example, theory of
programming languages. The situation is much worse with technology. There is no even a generally
accepted definition of technology. In spite of being one of the central phenomena of the modern
civilization, the concept 'technology' is a difficult to define. That is why the main emphasis of this
work is on technology and its mathematical theory. There are many mathematical models for different
technological processes. However, being useful, such models reflect only some parts of the whole
process. Complete multifaceted models of technological processes are developed in the mathematical
theory of technology. The development of technological knowledge and advancement of mathematics made it
possible to elaborate the mathematical theory of technology (Burgin, 1997). Its starting point is an
exact, however, informal definition of technology as a specific system of knowledge that describes how
different systems are produced, utilized or function. Basing on this definition, two classes of
technologies (the general and specific technologies) are introduced to reflect the situation existing in
industry and engineering. This provides for the construction of a general mathematical model of a
specific technology as well as for the development of a relevant mathematical apparatus and exact
methods for an investigation and design of various technologies (in industry, management, information
processing and so on). In the context of systems, which are created and used by people, the basic
concept of the theory, a technological operator reflects operations perfgd by people and machines, as
well as natural processes that are included in a technological process. For example, biotechnologies
utilize biological processes, while chemical technologies are based on chemical processes. In this
context, a general technology is a system of knowledge about a class of specific technologies, e.g.,
biotechnologies or information technology. The mathematical theory of technology utilizes new
mathematical disciplines such as theory of named sets, fuzzy set theory, and theory of structured
multidimensional models of systems and processes as well as traditional fields such as algebra, theory
of probabilities, and theory of algorithms. In the mathematical theory of technology, such problems as
stability, reliability, equivalence, constructibility, and realizability of technologies are studied.
The aim is the development of efficient methods and algorithms for design and improvement of different
technologies. 

 Galina Bushueva- Institute of Eye Diseases and Tissue
Therapy, Odessa, Ukraine

Psychology-Physiological Approach for the Analysis of the states

Pathology. Galina Bushueva, Nadia Kabachi and Arnold Kiv

 All pathologies are complex disturbances of physiological processes and deep changes in their
psychological state. In our presentation we would like to put forward a conception of complex
diagnostics of different diseases based on analysis of patients' physiological and psychological
characteristics. In [1] it was found correlations between the states of people cardio-vegetative system
and their creative thinking characteristics. These correlations are caused in particular by disturbances
of relaxation time of spasmodic state (RTSS) of organism. The results obtained in [1] opened a
possibility to study relaxation processes in the vegetative nervous system using a special device
(computer pupillograph), which gives quantitative characteristics of accommodation - convergence
pupillary (ACP) system of eye. In this work we studied patients with symptoms of disturbances in
vegetative nervous system. We investigated RTSS by measurement ACP system of eye at different stages
from beginning until the end of illness. At the same time we performed computer testing of creative
thinking. The method of testing programs development is described for example in [2].By computer
pupillograph we obtained data about pupillary size, reaction to illumination, accommodation and other
parameters of eye. We measured visual acuity, reserves of accommodation and the pupillary sizes at
convergence. Measurements of the vegetative nervous system tonus were fulfilled by method described in
[3]. Parameters of creative thinking, which were measured, are: I (Intuition), L (Logic), VTS (Volume of
thinking space). It was found in this study that in the process of treatment of patients an improvement
of creative thinking parameters mostly forestalls an appearance of usual signs of recovery, which are
used in medical practices. So it may be concluded that a complex psychology - physiological diagnostics
allows to define more precisely the end of recovery and the necessary time of treatment of patients for
an illness. We continued our research by trying to exploit the data obtained previously (results) to
predict the patients psychological behavior after their cure. For that, we are using generally the Data
Mining techniques [4] and in particular the Bayesian networks (probabilistic networks) which combine
probability theory with a graphical representation of domain models. The first results obtained we
seemed promising. The results can be used as an element in decisions of organizational and economic
problems in public health. REFERENCES 1. N. Bushueva, A. Kiv, V. Orischenko, E. Ushan, G. Finn and Sue
Holmes. Psychological limitation of creative activity. Computer Modeling&New Technologies 3 (1999)
135-137 2. A. E. Kiv, V.G.Orischenko et al. Computer Modeling of Learning Organization. In Advances in
Agile Manufacturing. Eds.P.T.Kidd, W.Karwowski. Amsterdam (1994) 553-556 3. A.M. Kluev. The State of
Vegetative Nervous System of People with Accommodation Spasm. Journ. of Ophtalm. 6 (1976) 443-45 4. N.
Kabachi, S. Levionnois, A. Happe, F. Le Duff, M. Bremond. Data Mining Techniques for Patient Care
Pathway. In Third International Conference on Data Mining Methods and Databases for Engineering, Finance
and Other Fields, 25 - 27 September 2002 Bologna, Italy (subdued). 

 Val Bykoski
- Boston University

Complex Models and Model-Building Automation

 A model-building framework (MBF) is proposed to automate building complex models. The framework is
designed to combine dynamically model components, analyze dependencies, construct a model code, build
it, connect the model to the input and output data, run the model, evaluate it, and, if necessary,
modify it. A construction environment and its control and configuration logic, a basic component of the
proposed framework, isdescribed. A generic model-building logic is presented and discussed as well as
building techniques and tools. Complex models such as business/enterprise models, geographical sites
such as cities, traffic systems (including air traffic), bioinformatics gene expression models, and
similar ones contain hundreds parameters, adaptive components, interdependencies. Initially, the
framework is a highly generic medium/space whichis being shaped and customized by the data into a
specific medium/space reflecting a structure and dynamics built into the data. The goal of a
model-building activity is to generalize the original data into equivalent ³model form² and to use the
model then to generate (³predict²) new data for a new context. This in fact is the goal of any
scientific framework, a ³theory². The architecture of a model used to be fixed, that is, equations,
functions involved (such as propagation functions, nonlinearity elements), dependencies, network
architecture, kernels, etc. Also, building a model used to be separated from the model running,
evaluation, and model modifying phase, a sort of offline data/model exploration pipeline. The MBF makes
those elements and phases highly dynamic. Construction tools² are necessary since complex models cannot
be hand-built. Since they cannot be solved ³analytically², in terms of symbols, the data-driven
technology to build a model is the only choice, indeed. The first step is a generic model prototype with
highly flexible/adaptive structural and functional components. It could be, for example, a cellular
neural networks (NN). Data as well as certain fundamental criteria/constraints are used to incrementally
build and customize the prototype making it a data container, a generating database which can absorb new
data and also to generate new data for new contexts. The data used to drive the model-building process
have to have a format ofinput-output (or if/then) pairs. So, the model-building process is ³supervised²
or driven by the output data. As an example, the NN models structure, their building rules,
interdependencies are discussed as well as integration of the (training) data into the framework. What
makes the MBF approach different is that the model is being built ³on-the-fly² based on a
meta-description, and therefore various models can be built ³to order² as a highly dedicated tool. The
MBF may as well generate the testing, visualization, and other appropriate tools for that model. A
prototype of a MBF is designed and developed, and versions of NN models have been generated
automatically using a generic core. Results will be presented and discussed. 

 Elena Bystrova - PhD student; Saint-Petersburg State University, Faculty of
Biology and Soil Sciences, Department of Biophysics

Fungal Colony Patterning as an Example of Biological Self-Organization. Elena Bystrova, Eugenia
Bogomolova, Anton Bulianitsa, Ludmila Panina, Vladimir Kurochkin

 One of the interesting examples of biological self-organization is the formation of different
spatiotemporal patterns in colonies of microorganisms such as bacteria, myxomycete Dyctiostelium
discoideum and fungi. We consider four main types of stationary dissipative structures, which can arise
in colonies of mycelial fungus Ulocladium chartarum - periodic rings (zones), sparse and dense "lawn"
(continuous mycelial growth), ramified (fractal-like) structures. We investigate conditions required in
order for patterns to appear and explain how changes in fungal environment influence the morphology of
the colony, or, in other words, how the system interacts with its environment. The experimental data
obtained enable us to construct a morphological diagram which demonstrates the morphological change due
to environmental conditions (in our case, we varied two parameters - nutrient concentration and agar
medium thickness). It has been suggested to consider fungal colony development as being determined by
two simultaneous processes - consumption of substrate (activator) and production of diffusible
metabolites (growth inhibitors). On the basis of proposed mechanism we developed a mathematical model
for description of observed non-linear phenomena in colonies of mycelial fungi. In computational
experiments we have revealed ranges of parameters in which the formation of zones and continuous
mycelial growth occur. In particular, metabolite diffusion coefficient was shown to be one of the main
parameters defining the colony morphology. By means of the model and experimental results we have
estimated the value of the given coefficient. 

 Raffaele Calabretta -
Institute of Cognitive Sciences and Technologies National Research Council, Rome, Italy

An Evo-Devo Approach to Modularity of Mind

 A module can be defined as a specialized, encapsulated organ that has evolved to handle specific
information types of enormous relevance to the species. Given their extremely general nature modular
systems can be found in different aspects and levels of reality, from genomes to nervous systems, from
cognitive and linguistic systems to social systems, and they are an object of study for a variety of
scientific disciplines, from genetics and developmental biology to evolutionary biology, from the
cognitive sciences to the neuro-sciences. Modules can emerge during the life of an individual organism
as part of the biological development of the individual. Biological development is controlled by both
genetically inherited information and the environment in which the individual develops. The two sources
of information do not simply add linearly but there is a strong nonlinear interaction between them.
Genetic expression is environmentally constrained in that the external environment (even in utero) and
the already produced phenotype at any stage of development have a crucial role in determining the
further developmental stages of the phenotype. And, conversely, learning, experience, and the
environment external to the organism's body can influence the phenotype only in ways that are, more
broadly or more stringently in different cases, genetically fixed. In any case it is clear that the
modular structure of the brain is strongly influenced by the species-specific information contained in
the DNA of all individuals of the same species. This poses two important research questions: How
information relating to the modular structure of the brain is encoded in the genotype? How is this
genetically encoded information acquired during an evolutionary process taking place in a succession of
generations of individuals? With respect to the first question we have to consider that the genotype
itself is a modular system. The DNA molecule can be analyzed as made up of modules at different levels:
bases, aminoacids, genes. If we consider genes as the genetic modules, we know that genes map in complex
ways into phenotypical traits. The mapping rarely is one-to-one but more frequently is many-to-many.
Many genes enter into the determination of a single phenotypical trait and at the same time a single
gene tends to influence many different phenotypical traits. Hence, we cannot in general assume that one
gene entirely controls the development of a single neural module but, more probably, many genes control
the development of one and the same module and one and the same gene controls the development of many
different modules. The second research question concerns the evolutionary origin of the genetic
modularity of the genotype which underlies the development of neural modules. For example, one can
hypothesize that at first a segment of the genotype controls the development of some particular neural
module; then, this segment of the genotype is duplicated as a result of some mutation; and finally, the
duplicated genetic module changes its function as a result of some further mutation and it becomes
responsible for a new neural module with a different function. For the purpose of answering the two
questions described above, it is obviously necessary to consider in the chosen model of study both the
process of organism development and organism phylogenetic history. However, it is surprising to notice
that the developmental and evolutionary integrated study of organism modularity is a quite recent
conquest even in biology. Traditionally, the process of organism development and that of phylogenetic
evolution were considered as being very different and thus have been studied separately in two different
research fields (i.e., developmental biology and evolutionary biology). In order to achieve a better
comprehension of reality, the importance of considering them at the same time it has instead been
recently pointed out (consider, for example, the recent symposium on Modularity in development and
evolution, Delmenhorst, Germany, May 11-14, 2000). The combined approach - now known as an "evo-devo"
approach - to studying the meaning of biological is very innovative and is therefore not yet widespread
in biology research, but it is showing itself to be plausible from a biological point of view and
therefore very useful from an heuristic point of view. Unfortunately also in the cognitive sciences,
development and evolution have not been considered as being two very important processes to be studied
together in order to get a better understanding of the modular nature of mind. On the contrary, they are
often considered as being two opposed processes that are incompatible for explaining the acquisition of
brain competence. On the one side, developmental psychology, while admitting the modular nature of mind,
claims that this is most of all the product of a process of development. On the other side, nativists
and evolutionary psychologists maintain the view that humans born already furnished with hardwired
cognitive modules. Without wanting to take a part in this controversy, which remains nevertheless one of
central interest in the cognitive sciences (i.e., the nature-nurture debate), we suggest a way to
reconcile different position in a new way of studying the evolution of modularity of mind, i.e., an
evo-devo approach. REFERENCES Calabretta, R., Di Ferdinando, A., Wagner, G. P. and Parisi, D. (In
press). What does it take to evolve behaviorally complex organisms? BioSystemsCalabretta, R. & Parisi,
D. (In press). Evolutionary Connectionism and Mind/Brain Modularity. In W. Callabaut & D. Rasskin-Gutman
(Eds.), Modularity. Understanding the development and evolution of complex natural systems. The MIT
Press, Cambridge, MA. [draft: doc, pdf; updated June 4, 2001] Calabretta, R., Nolfi, S., Parisi, D. and
Wagner, G. P. (2000). An artificial life model for investigating the evolution of modularity. In Y.
Bar-Yam, (ed.), Unifying Themes in Complex Systems. Perseus Books, Cambridge, MA.Di Ferdinando, A.,
Calabretta, R., and Parisi, D. (2001). Evolving modular architectures for neural networks. In R. French
& J. SougnÈ (Eds.), Proceedings of the Sixth Neural Computation and Psychology Workshop Evolution,
Learning, and Development, Springer Verlag, London. [pdf, ps, abstract]Fodor, J. (1983). The modularity
of mind. The MIT Press, Cambridge, MA. Karmiloff-Smith, A. (1999). Modularity of mind. In R. A. Wilson
and F. C. Keil (Eds.), The MIT encyclopedia of the cognitive sciences. The MIT Press, Cambridge, MA.
[html]Keil, F. C. (2000). Nativism. In R. A. Wilson and F. C. Keil (Eds.), The MIT encyclopedia of the
cognitive sciences, The MIT Press, Cambridge, MA [html]Simon, H. A. (2000). Can there be a science of
complex systems? In Y. Bar-Yam, (ed.), Unifying Themes in Complex Systems. Perseus Books, Cambridge,
MA.Wagner, G. P., Mezey, J. & Calabretta, R. (In press). Natural Selection and the origin of modules. In
W. Callabaut & D. Rasskin-Gutman (Eds.), Modularity. Understanding the development and evolution of
complex natural systems. The MIT Press, Cambridge, MA [draft: doc, pdf; updated March 13, 2001]Wallace,
A. (2002). The emerging conceptual framework of evolutionary developmental biology. Nature 415, 757-764.


 Mathieu Capcarrere - Logic Systems Laboratory. Swiss Federal Institute
of Technology, Lausanne

Emergent Computation in CA: A Matter of Visual Efficiency

 Cellular Automata as a computational tool have been the subject of interest from the computing
community for many years now. More precisely, the development of the Artificial Life field led many to
wonder on how to do computation with such tools. Artificial Evolution which gave good results on
specific tasks, like density or synchronization was often given as an answer. However, it appeared that
the limitations of such an approach were severe and really the question of WHAT meant computation with
cellular automata became pregnant. The answer to this question is far from obvious. Mitchell,
Crutchfield, Hanson et al proposed an analysis of "particles" as a partial answer. Wuensche more
recently developed the Z parameter as a paraphernalia to treating this question. Before this question
appeared in its full-blown form in the A-life/Computer scientist community,there were already
propositions going this way with Wolfram's class III and, related, Langton's computing at the edge of
chaos. In this presentation/paper, I will argue that computation of CAs is a matter of visual
efficiency. Basing our argument on past, recent and also previously unpublished results (ours and
others) mainly but not only on the density and the synchronization task, I will propose a definition of
what is computation by means of CAs. This will be the occasion to (re)define emergent behavior, in a
limited scope, but also to envisage differently the whole question of what may be sought in computing
research in CAs. The practical consequences of this approach will alter the HOW question answer, and
most notably how to evolve computing CAs. Though the talk will be centered around the density task, will
encompass a much bigger chunk of the CA field. However, the claim is NOT a redefinition of the whole
computation by means of CAs, but rather a tentative definition in the limited scope of emergent
computation. 

 Giuseppe Castagnoli - Elsag, IT Division

The Quantum Computer: A Complex System Irreducible to Classical Models

 All devices created by man to support his everyday life, from the stone age to modern engineering, are
functionally representable in classical physics. The quantum computer might make the first exception.
Its operations essentially call for a quantum mechanical representation (Mahler 2001). They fully draw
on a richness of quantum physics that vanishes in classical physics. Being a complex and purposeful
(problem-solving) system strictly based on non-classical laws, the quantum computer as a model might
enrich the vision of Complex Systems. We provide here a pedagogical presentation. By omitting
technicalities, the special way of working of the quantum computer can be explained in a conceptually
complete fashion to an interdisciplinary audience. We start by explaining the special quantum effects
used in to-day quantum computation: quantum mode superposition (through the metaphor of the
parallel-possible universes), parallel non interfering and interfering histories, the exponential
explosion of the number of modes/histories of a compound object with the number of its parts, quantum
togetherness (entanglement), quantum measurement as filtering. Pictorial aids illustrating possible
histories and their quantum transformations substitute mathematical formulation. Then we show in
conceptual detail how the above special effects yield the quantum speed-up of Shor's (1994) and Grover's
(1997) algorithms. Speed-up can mean solving in less then a second problems whose solutions by to-day
classical computers would in principle require billions of billions of years. Long-standing
computational notions become deeply altered in quantum computing, starting from the very notion of
algorithm as a procedure for dynamically constructing the solution of a problem. In quantum computation,
the implicit definition of the solution, inherent in the statement of the problem, directly determines
the solution in a non procedural way, through an extra-dynamical quantum transition. Such a transition
is jointly influenced by the initial and the final selection (Castagnoli and Finkelstein, 2001). This is
unlike classical computation which is the dynamical development of the initial selection (i.e.
condition) alone. We discuss how the current approach to quantum computation might hold a classical
vestige, being still algorithmic in character, although enriched with some special quantum effects. We
introduce at a conceptual level a non-algorithmic, non-dynamical approach to quantum computation (Jones,
2000, Castagnoli and Finkelstein, 2002). This can be based on the same quantum relaxation processes and
symmetries (due to identical particle indistinguishability) that govern the formation of atomic and
molecular structure. This latter approach naturally raises the question whether biological processes and
molecular evolution draw on the richness of the quantum level. REFERENCES: G. Castagnoli and D.R.
Finkelstein (2002) Quantum-statistical computation arXiv:quant-ph /0111120 v4 30 Jan 2002. G. Castagnoli
and D.R. Finkelstein (2001) Theory of the quantum speed-up Proc. R. Soc. Lond. A 457, 1799.L. Grover ,
(1996) In Proc. 28th A. ACM Symp. On Theory of Computing, P. 212. Philadelphia, PA: ACM Press. J. A.
Jones, V. Vedral, A. Ekert, G. Castagnoli (2000), Nature 403, 869.G. Mahler (2001) Science 292, 57.P.
Shore (1994) In Proc. 35th A. Symp. of the Foundation of Computer Science, Los Alamitos, CA, P. 124. Los
Alamitos, CA: IEEE Computer Society Press. 

 Alok Chaturvedi - Purdue
University

Synthetic Environments for Analysis and Simulations. Alok Chaturvedi and Shailendra Raj Mehta

 SEAS is a distributed, interactive, real-time synthetic economy populated with human and artificial
agents. It allows realistic representations of markets and economies at any level of detail. The
artificial agents represent decision-makers who engage in relatively non-strategic decision making, such
as consumers in large markets. Human agents, on the other hand, represent decision-makers such as firms
and governments that engage in strategic interaction, and are provided with extensive decision support
systems to make effective decisions. This combination of human and artificial agents, unique to SEAS,
allows for the creation of environments that combine complexity and realism. In addition, we
systematically collect data on costs, demands, market shares, demographics and technological trends and
calibrate SEAS to replicate the economic or management situation under consideration. On account of
these capabilities, SEAS has exciting uses in business "war gaming" and training. It is structured
around the interplay of human decisions and game events that requires active involvement of
participants. It helps participants come to a more complete understanding of the sources and motivations
underlying the decisions by placing them in the shoes of executives running the firms at different
points in time. Games dealing with current or future situations help explore the potential implications
of various courses of action, and raise important questions for further investigation. 

 H. F. Chau - University of Hong Kong

How To Avoid Fooling Around In Minority Game H. F. Chau and F. K. Chow

 Minority game (MG) [1][2] is a simple model of heterogeneous players who think inductively. It plays a
dominant role in the study of the global collective behavior in free market economy in econophysics
since it is a powerful tool to study the detailed pattern of fluctuations. In MG, there are three
important parameters: the number of players N, the number of each player’s strategies S and the
length of histories M. Maximal cooperation of players is observed in MG whenever 2^M » NS. However, is
it possible to keep optimal cooperation amongst the players for any fixed values of N, S and M? We
report a simple and elegant way to alter the complexity of each strategy in MG with fixed N, S and M so
that the system can always be locked in a global cooperative phase. Indeed, our investigation concludes
that player cooperation is the result of a suitable sampling in the available strategy space. [1] D.
Challet and Y.-C. Zhang, Physica A 246, 407 (1997). [2] Y.-C. Zhang, Europhys. News 29, 51 (1998). 

 Adrian Li Mow Ching - University College London

User and Service Interaction Dynamics. Adrian Li Mow Ching, Venus Shum and Lionel Sacks

 Active networks enable a faster deployment of services in a telecommunications network. To develop
autonomous management systems for such a network requires understanding the emergent behaviour arising
from the interactions between the underlying users and the network services. In this paper we use
complex systems modelling to perform service engineering analysis on a generalised telecommunications
service network abstracted from an ANDROID network. This work aims to develop ideas and issues regarding
service engineering for future networks. In particular, we analyse the interactions between users and
the network and study the affects of load balancing on the service platform. As a result we see a
self-limiting characteristic that prevents the load on the communications network increasing beyond its
capacity. However, the resulting effects on the users are an increase in the volatility of the response
time for each service request, which is highly undesirable. We find that load balancing has significant
improvements on resource utilisation and load management. 

 D. Chistilin -
Institute World Economy and International relation

Development and Self-Organization of Complex System. Case of World Economy

 This work is an intermediate result of a research of the process of development and self-organization
of world economy; this research was begun in 1998 and it uses a complex systematic approach (the theory
of complex systems). Some elements of the work were presented at scientific forums of NECS ( ICCS-98,
ICCS-2000). The objective of the work is to reveal and define phenomena which are characteristic to the
behavior of complex systems in the process of their development on the basis of factual material of the
world economy development during the period of 1825-2000. The following methods are used in the
research: complex-systematic analysis, historical and interdisciplinary methods and method of simulation
in verbal-logical form. Social system - "the world economy" - is considered as a complex system
consisting of two global subsystems: economic and political. Common agents for both subsystems are
national economies, which interact in economic and political spheres and form connections and structure
of social system of world economy. The principal functional purpose of social system "the world economy"
is an achievement of self-regulation of relations between the agents in economic system through
political system which results in the state of dynamic equilibrium, i.e. economic growth of world
economy. The functioning of world economy means an implementation of economic and political relations in
the process of international exchange of resources on the basis of international division of labour with
the aim of the most effective distribution of valuable resources for production of valuable benefits for
consumption. Development of social system means a process of increasing its stability under the
influence of outside environment (maintenance of stability in the given limits - homeostasis) by means
of accumulation of structural information changing the quantity of organization (effectiveness) of the
system and making its structure more complicated. Increasing of stability is expressed in accumulation
of economic effectiveness and formation of more complicated structure of society. Development of world
economy means a co-evolution of economic and political subsystem development resulted in gradual
complication of social order, which increases the system stability under environment influence, pressure
of population growth and resources limits. Development means a change of equilibrium states with
different macro-economic characteristics. Each state is expressed in structural and quantitative
characteristics. For world economy we will consider as the system of international monetary relations
(IMR) as a structural characteristic. The growth rate of the gross national product of the
countries-members of international economic relations we will consider as a quantitative characteristic.
On the basis of historical material we distinguish three structural characteristics of IMR type and,
correspondingly, three periods of time and three states of world economy (gold standard, system of
Á›åòîí-Bóäñ, Jamaika system). On the basis of statistical data we calculate the quantitative
characteristics for each state. Then we bring all data together into the table forms. On the basis of
distinguished states we build verbal-logical model of the world economy (figure). As an specimen we take
the ‹.Àâäååâ's model, which is based on the idea that phenomenon of development can be considered as a
struggle of two opposite trends - organization and disorganization. The process of development, which is
begun from the maximum of disorganization, can be described as a process of accumulation of structural
information, which is calculated as the difference between the real and maximum values of entropy. The
model allows to make the following conclusions: 1. Each consequent distinguished state of world economy
has more complicated organization of political system and international monetary relations. And this
fact demonstrates the tendency of complication of world community structure. 2. Each consequent state is
more effective from economic point of view and has the higher rate of economic growth. This allows world
economy to develop steadily in conditions of grown population of the planet and limited resources. A
tendency of growth of economic effectiveness of the whole system is observed in the long-lasting
interval of time. 3. The process of formation of consequent structures of both political and economic
organization of the world economy took place in condition of strong non-equilibrium environment resulted
in numerous military and civil conflicts and economic crises. 4. All stated above allows to conclude
that: system of the world economy possesses the property of the complex systems - self-organization. the
Îêñàíãå›à-Ï›èãîæèíà's principle of minimum of energy dissipation is realized in the process of
development. Each consequent organization of world economy produces less entropy than the previous one.
The category of energy in physical system corresponds to category of resources in social system. Thus,
principle of minimum of dissipation of limited resources acts in social systems. The model reflects
realization of this phenomenon in the process of development and self-organization of the world economy.
Direction of development of the system "world economy" is defined by the law of conservation of
accumulated effectiveness and this allows to say that the model has predicted potential for realization
of prognosis of future organization of the world economy. 

 Claudia
Ciorascu - University, Iasi, Romania

The Accuracy of Auto-Adaptive Models for Estimating Romanian Firm's Cost of Equity. Claudia Ciorascu &
Irina Manolescu

 In the paper the mutations of the Romanian firms capital structures and the relations with the cost of
equity capital are analyzed. The validity of leverage effect of capital structure to financial return is
also tested. We consider in this analysis the public data of more than one hundred Romanian firms listed
at Bucharest Stock Market and on RASDAQ, between 1997 and 2000. The initial hypotheses in this research
are: the relation of leverage effect is not validated for Romanian firms; using auto-adaptive models for
the estimation of firm’s cost of equity the results are better than the classical techniques (as
CAPM or auto-regressive models). Because of their adaptation to the specific of the input data, the
self-adapting models can be successfully used in real problems with large data sets. We are studying the
quality of this approach in the case of estimating firm’s cost of equity. This also defines the
pre-requisites for considering new instances of the problem: share valuation, investment
decision-making, etc. The presence of the determinant factors of capital structure (bankruptcy and
monitoring costs, motivation of the managers, institutional restrictions, transaction costs, taxes)
represents an argument for the validation of the first hypothesis, but their incidence is exceeded by
the low liquidity of the Romanian capital market. All this elements have opposed effects on the cost of
capital and the resulted conclusion is the impossibility to obtain an optimal capital structure,
especially on Romanian capital market. The existence on Romanian stock market of the listed firms with a
debt ratio (debt / equity capital) higher than 500%, but sometimes reaching incredible values like
10000%, raises serious questions over the admittance criterions on stock market quotation. These highly
indebt firms have descending trends in activity and profits and the questions about financing policy can
be raised both firm and financial institutes level. 

 Michael Connell -
Harvard

Neuroscience and Education--Bridging the Gap

 Many researchers, educators, and laypersons are excited about the prospect that neuroscience can
fruitfully inform educational research and practice, ultimately leading to significant improvements in
curriculum design, pedagogical techniques, and overall efficacy of educational institutions. Despite the
enormous amount of hype surrounding these issues, however, to date there have been few tangible,
rigorous results demonstrating how findings from neuroscience can actually be brought to bear on
educationally relevant problems such as mechanisms of knowledge transfer. In this presentation, I argue
that computational neuroscience offers tools that can provide a theoretical bridge for applying
neuroscience findings to educational issues. The approach I describe involves identifying key
constraints at the neurological level (i.e. that structural changes to synapses are implicated in
long-term storage of information encoded in the nervous system, whereas dynamic functional activation
patterns seem to be the basis for thought and action), incorporating these assumptions into a
computational neuroscience model (a connectionist or other kind of artificial neural network), and then
tracing the effect of these (biologically plausible) low-level constraints on higher-order patterns (at
the level of cognitive processes), attempting to filter out model characteristics and behaviors that are
mere artifacts of the model and its more ad hoc assumptions. This complex systems approach provides a
straightforward way to link two levels of analysis (neural structure and cognitive function) that is
complementary to the more common reductionist approach of building detailed models of specific phenomena
(such as language acquisition or concept formation), and may be particularly attractive at the present
time for people interested in exploring qualitative properties of higher order cognitive functions
involving neural structures that are far removed from the sensory periphery. I describe how
computational neuroscience offers a non-intuitive (and biologically informed) paradigm for understanding
human knowledge organization and conceptual structure (in terms of a semi-parametric representational
substrate), and I describe how it can shed new light on some educationally relevant issues. In
particular, I discuss how this model provides insight into the nature of representation in the mammalian
nervous system, I argue that this analysis suggests a set of meaningful conceptual categories that can
inform meta-theoretic reasoning about classes of psychological theories, and I describe the mechanism of
stimulus generalization (near transfer) that is revealed by this model. 


Emilia Conte - Polytechnic of Bari

Managing Urban Traffic Dynamics by a Multi-Agent DSS

 Complexity of urban environments is severely challenging science and technology, making Information
Technology (IT) tools strongly significant for enabling innovative and more appropriate decisions.
Concerning urban environments, the paper deals with the important issue of the traffic dynamics
proposing a tool for managing and controlling traffic air pollution, since its effects on human health
are recognized as widely relevant. The design of the proposed tool is based on the belief that IT
systems can support decision making processes reducing routine tasks thus enlarging substantive and
problem oriented decision activities, through improving man-machine interaction. Basing on a local case
study, the paper reports about a two-years research work of the authors, studying traffic problems in a
middle-sized city of Southern Italy, starting from daily monitored data about air quality conditions.
The research was aimed at developing the architecture of a Decision Support System (DSS) assisting
decision makers of municipal offices to implement strategic actions for controlling traffic air
pollution. The DSS is designed as a multi-agent system in order to replicate the single and autonomous
tasks of the decision makers, at the same time saving and strengthening the interaction among those
tasks and fuelling dialogue among different data sources and receivers. The paper describes the
development of the DSS architecture which was carried out starting from the real structure of both the
decision making process and its main activities and focusing on the use and the production of knowledge
within the process itself. In the DSS design, a special attention was given to three main tasks of the
decision process: validation of data from pollution measurement stations, assessment of measurement
stations functioning, and production of short term traffic actions. A neural network approach and an
expert system environment represent the main technological basis for the implementation of the DSS.
Further research perspectives are finally investigated in the wider dimension of medium/long term
traffic control, where strategies can benefit by the multi-agent interactive approach, which can lead to
gradually shift to different, higher, levels of organization in problem management. Some considerations
are made with regards to knowledge representation tasks within DSSs devoted to complex problem
management, to the use of machine learning as a form of knowledge representation, and to organizational
learning within the decision making environments facing air quality problems. 


Ron Cottam - Brussels Free University

Self-Organization and Complexity in Large Networked Information-processing Systems. Ron Cottam, Willy
Ranson & Roger Vounckx

 Classical analysis of large networked information-processing systems from a ³quasi-external² point of
view begins to create problems as the range of hierarchical structural scales is extended. Most
particularly, the viability of deterministic distributed control becomes questionable in extended-scale
temporally-dynamic (i.e. interesting!) networks. The ³traditional² split between ³body² and ³mind²
appears to be most particularlyrelated to our mental incapacity to relate to large systems whose
character is primarily distributed but whose characteristics collapse to those of a synchronous
deterministic network when reduced to a unified perspective. The major problem in forming such a
representation is the necessarily irrational coupling across multiple scales of a large disparate
complex organization such as the brain and our consequent inability toformulate correctly a causal tree
for the system. We investigate the implications of these difficulties beyond simply the establishment of
an upper systemic scaling limit, and relate them to a recently recorded Windows Local Area Network
browser election breakdown. 

 D. E. Creanga - Univ. Al.I. Cuza

Computational Analysis in Temporal Series Describing Kidney Excretory Function. D.E. Creanga,
E.Lozneanu, J.C.Sprott

Semi-quantitative analysis was carried out on the excretory function of human kidney. Health and
pathological kidney are investigated by means of nuclear medicine using radio-pharmaceutical technique
based on radio-isotopeTc99m and a gamma-camera device assisted by a specialized computer. The amount of
Tc99m physiological solution in every kidney is given by a temporal series computationally which we
processed using the analysis strategy based on linear and non-linear tests. Fast Fourier transform,
auto-correlation function, the portrait in the phase space and the corresponding fractal dimension
present similarities as well as differences when the health kidney is compared to the pathological one.
The histogram of probability distribution is presenting a repetitive character for the normal kidney
while for the non-functional kidney the strong asymmetry of the probability distribution is the dominant
feature of the histogram shape. Fast Fourier transform does not present significant differences nor in
the lin-log representation neither in the log-log representation but the wavelet transform seems to be
marked by some qualitative differences. The portrait in the state space reconstructed using the first
derivative of the raw data graph shows a higher dispersion of the points corresponding to the ill kidney
than for the health one (while the reconstruction using the delay coordinates appears in the same form
for both cases). The most important difference between the two cases is offered by the correlation
dimension which is significantly higher for the health kidney in comparison to the ill one. 

 Atin Das

Nonlinear Data Analysis of Experimental [EEG] data and Comparison with Theoretical [ANN] Data

 In this paper, nonlinear dynamical tools like largest Lyapunov exponents (LE), fractal dimension,
correlation dimension, pointwise correlation dimension will be employed to analyze electroencephalogram
[EEG] data obtained from healthy young subjects with eyes open and eyes closed condition with the view
to compare brain complexity under this two condition. Results of similar calculations from some earlier
works will be produced for comparison with present results. Also, a brief report on difference of
opinion among coworkers regarding such tools will be reported; particularly applicability of LE will be
reviewed. The issue of nonlinearity will be addressed by using surrogate data technique. We have
extracted another data set which represented chaotic state of the system considered in our earlier work
of mathematical modeling of artificial neural network. We further attempt to compare results to find
nature of chaos arising from such theoretical models.

Neural Net Model for Featured Word Extraction A Das, M.Marko, A. Probst

 Existing search engines have many drawbacks while situations demand more refined operations. We have
shown with examples that two of the mostpopular conventional search engines return out-of-context
results. Our group is actively engaged in developing new algorithms for this purposewhich are different
in understanding the topology of searching. There are two main independent approaches to achieve this
tasking. The first one, using the concepts of semantics, has been implemented partially.(For more
details see another paper presented by at the conference: Transforming the World Wide Web into a
Complexity-Based Semantic NetworkM.Marko, A.Probst, A.Das.) The second approach is reported in this
paper. It is a theoretical model based on using Neural Network (NN) learning features. Instead of
usingkeywords or reading mechanically words from the first few lines from papers/articles, the present
model gives emphasis on extracting 'featuredwords' from an article. We call those words as 'featured
words' that occur most frequently. Obviously we have to exclude English words like "of,the, are, so,
therefore" etc. from the list of featured word. To form such a word list, an article is read first. To
read a full paper as input tothe model would be a heavy load of computation, so a choice of the first
few hundred words can serve the purpose -also because beyond this limit,generally technical or
scientific notations appear which are not relevant for the present purpose. These words are raw data and
will be used asthe input to the model. The NN model will chose from N such words (raw data) to find M
featured words (refined data). This output is then fed tothe search engine to produce more accurate
words. Working of the proposed NN model is based on Principal Component Analysis (PCA). Another
important feature of the proposed model is an association of words so that when related words combine to
form a meaningful word which maynot be in the user-supplied list of search terms- is also included in
the featured word and hence in the search result. We also propose to giveweights to the exact place of
occurrence of a word. For example those in the headline or as "key word" are more important than those
in the bodyof the text. Finally, a scheme is proposed to train such a network to accomplish the entire
scheme outlined above. This will be implemented by managing theweight matrix of the proposed model. 

 Christopher J. Davis - Carnegie Mellon Univ

Biology, Brains and Catalysis

 In this talk, I examine the hypothesis that living processes, from the smallest to the largest
(including the brain), are catalytic processes, and hence, that life is a fractal catalytic process.
Although a physical thermodynamic process, I suggest that catalysis is not confined to enzymatic
biochemistry; rather, enzyme catalysis is a microscopic example of a general process. It is suggested
that rather than characterizing biological processes in terms of 'functions,' they are better
characterized in terms of their property of persistence. The persistence of a living process, which may
be chemical, neural, perceptual or behavioral, is a direct consequence of the catalytic process that
makes 'explicit' the orders and relationships that are 'implicit' in the environment. Because catalysts,
and therefore processes of catalysis, emerge unchanged from the reactions that they mediate, there is a
relation between catalysis and persistence; this relation is most evident in living processes. Moreover,
the persistence of a catalyst may be a consequence of the way in which it mediates the
chemical/thermodynamic tendencies in its environment. This process may involve the order (or structure,
possibly related to the entropy) of the system. In the domain of enzyme catalysis, several researchers
have theorized that the principle agent of catalysis is a type of wave called a soliton (e.g., Caspi &
Ben-Jacob, 2002; Sataric et al., 1991). Solitons are waves that can travel large distances without
significant loss of energy or structure. It is argued that this property is consequent on the
relationship between the soliton and the structure (or boundary conditions) of the medium. In the case
of enzyme catalysis, the soliton is expressed as a vibrational mode involving the molecules that
comprise the enzyme. It will be argued that the persistence of the soliton and the persistence of the
enzyme are necessarily related. A soliton may provide a path between the 'before' and 'after' energy
states via points of ambiguity or'fixed points' (i.e. points that do not change), which may be related
to the 'order' (or structure) in the environment. I will discuss how the soliton wave has been
implicated in several levels of biological process including enzymes, DNA, microtubules, heart function,
muscle function and nerve impulses, evidence that lends support for the fractal catalytic theory. The
fractal catalytic hypothesis is consistent with (but more specific than) the 'order from order'
principle of Schrodinger (1979), who suggested that living systems maintain their order by utilizing the
order in their environments. At the same time, the present hypothesis contrasts with the 'emergentism'
of chaos theory, which fails to address the relationship between far-from-equilibrium dynamic systems,
as may be observed at the level of the brain, and the other systems (both internal to the organism and
external) with which it must interface. Finally, the hypothesis of an abstract but unifying process,
such as catalysis, that characterizes all of life challenges the implicit assumption of theoretical
biology that the strategies and processes that characterize a species are completely contingent upon the
chance evolution of a process that reproduces and mutates. 

 Matthew T.
Dearing - Cornell University

Digitally Mapping Cultured Neuron Networks Matthew T. Dearing , Harold G. Craighead

 An understanding of structural and functional characteristics in a complex network requires a detailed
map of the network's components and interconnections. Data sets representing the Internet,
World-Wide-Web, scientific collaboration networks, and biological processes have been used to analyze
these systems' network characteristics. However, there has been a lack of sufficient architectural data
for another interesting complex network system: interconnected neurons. We present a method of automated
digital image analysis to extract critical network properties from a cultured neuron network. Our data
collection software will provide needed information for mapping cultured neuron systems, which will
later be systematically compared to the functional characteristics of neuron devices with similar
network structures. 

 Ronald DeGray - Saint Joseph College

Developing a Web-based Interactive Syllabus for an Undergraduate Course in Systems Thinking and
Complexity

 We will demonstrate a web-based interactive syllabus for a one semester course in systems thinking and
complexity that is suitable for acapstone course in an undergraduate curriculum in information
technology. We also report our pedagogical and epistemological experiences in developing the course
content. We wanted to provide students with a cognitiveframework beyond the technical aspects that they
would acquire from a text based Information Technology curriculum. We also wanted to takeadvantage of
technology and the rich source of materials available on the Internet. This will be a joint presentation
by Saint Joseph College professors: Dr. Ronald DeGray, Associate Professor of Mathematics and Dr.
ShyamalaRaman, Associate Professor of Economics and Director of International Programs 

 Eugenio Degroote - Universidad Plitecnica de Madrid

Flame Spreading Over Liquid Fuels: A General Model

Understanding flame spreading over liquid fuels is a matter of both fundamental interest and crucial
practical importance, mainly for its relevance to safety issues, as it is the base for the knowledge and
ultimate control of fire propagation/suppression mechanisms.In this work, a complete overview of the
problem is shown. A complete set of experiments have been carried out; they show that, depending on the
initial surface fuel temperature, at least five different spreading regimes have been found. The
appearance of all these regimes is directly related with the existence of a preheated zone (in the
liquid phase) in front of the flame, that contributes in some way to its spreading. Our last
experimental results suggest that the different mechanisms involved in this process are also directly
related with the thermal transfer between the liquid phase (Liquid fuel) and the gas phase (fuel+air);
this interaction between both phases seems to be the main responsible of the existence of so many
differente spreading regimes. Finally, considering flame spreading as a dynamical system, the different
transition temperatures observed have been observed and characterised as well. A numerical model is
being proposed, that fits well with our experimental results. A dimensionless variable has been defined
too, that results to be very similar for al the fuels and exerimental geometries used in our
experiments, for one of the critical temperatures observed. 

 William A.
Dembski - Baylor University

Why Natural Selection Can't Design Anything

 In the early 1970s Leslie Orgel argued that the key problem facing origin-of-life researchers was to
explain the specified complexity inherent in the first living form. Thirty years later this remains the
key problem facing origin of life research. Nonetheless, the biological community is convinced that the
specified complexity of living forms is not a problem once replication is in place and the Darwinian
mechanism has become operative. In this paper I argue not only that we have yet to explain specified
complexity at the origin of life but also that the Darwinian mechanism fails to explain it for the
subsequent history of life. To see that the Darwinian mechanism is incapable of generating specified
complexity, I consider the mathematical underpinnings of that mechanism, evolutionary algorithms.
Roughly speaking, an evolutionary algorithm is any well-defined mathematical procedure that generates
contingency via some chance process and then sifts it via some law-like process. It is widely held that
evolutionary algorithms provide a computational justification for the Darwinian mechanism of natural
selection and random variation as the primary creative force in biology. Nonetheless, careful
examination of evolutionary algorithms and the informational constraints wherewith they are programmed
reveals that evolutionary algorithms, far from eliminating the specified complexity problem, merely push
it deeper. I employ Wolpert and Macready's No Free Lunch results to show that any output of specified
complexity from an evolutionary algorithm presupposes a prior input of specified complexity. And since
all biological design exhibits specified complexity, it follows that evolutionary algorithms (and the
Darwinian mechanism in particular) are incapable of resolving the problem of biological design. 

 Tessaleno C. Devezas - Los Alamos National Laboratory

Aggregate Output Model Describing the Limit-Cycle Behavior of a Logistic Growing Process in
Socioeconomic Systems

 A socioeconomic system is an evolving complex adaptive system with many kinds of participants, which
interact in intricate ways that continually reshape their collective future. During the ongoing
evolutionary process the system self-organizes and learns configuring and reconfiguring itself toward
greater efficiency among greater complexity. Each stage of the evolutionary process corresponds to a
given structure that encompasses previous self-organization, learning and current limitations. This is
to say that self-organization and learning are embodied in the system's structure and the learning rate
is an overall system's property. Such stages of the evolutionary path of a socioeconomic system are well
described by simple logistic curves that to some extent conceal the complexity of mechanisms involved In
this paper a cybernetic framework is proposed which, using a chaos based approach, may help to unveil
some hidden properties of the logistic learning collective dynamics. From the relationship between the
differential and the discrete logistic equations, it is demonstrated that the unfolding of a logistic
(learning) process is constrained by two control parameters: the aggregate learning rate  and a
generation-related characteristic time tG, whose product maintained in the interval tG<4 (deterministic
chaos) grants the enduring evolutionary process. Describing the socioeconomic system discretely as a
logistic growing number of interactorsadopting a new set of ideas (new learning) and using the logistic
function as the probabilistic distribution of individuals exchanging and processing information in a
finite niche of available information, it is demonstrated that the rate of information entropy change
(K-entropy) exhibits a four-phased limit-cycle behavior. Implications of this modeling on reducing
logical uncertainties in predicting the behavior of social systems are discussed. 

 Solomon Gilbert Diamond - Harvard University

Measuring Hypnosis: Relating Mental State to Systematic Physiological Changes

 A fundamental problem in hypnosis research is to quantitatively assess the hypnotic depth of subjects
because the subjective experience of patients during hypnosis cannot be measured directly. Prior
evidence exists that systematic physiological changes during hypnosis may be reflected in heart rate
variability (HRV). A novel method is presented for estimating HRV parameters that change dynamically on
the time scale of seconds. The estimated parameters are combined into a single normalized HRV dynamic
parameter (nHRVdp). This parameter was found to increase systematically across 10 subjects during the
hypnotic state when compared with a commensurate control condition (p<0.000001). Significant
correlations were found between the number of subjective hypnotic phenomena experienced by subjects and
mean nHRVdp (p=0.043). Dynamic self-rating of hypnotic depth during hypnosis was also found to correlate
significantly with nHRVdp (p=0.0497). These results suggest that an ECG monitor together with the
proposed algorithm can objectively measure hypnotic depth. This "hypnometer" could have broad
applications in clinical hypnosis and in research to better understand the physiology of the hypnotic
state. 

 Commander John Q. Dickmann - US Navy

Complex Systems Research and Information Age Warfare

 Defense community innovators have proposed concepts for using cutting-edge technologies to solve long
standing military challenges, including destruction of time-critical targets, theater-wide surveillance,
power projection and access to littorals. These concepts assume great benefits from networking that will
enable military advantage by use of distributed systems. However, the advantages of networking as well
as the implications of engineering distributed systems have not been fully articulated. This paper
defines and describes how distributed, networked forces provide advantage; translates the advantage to
engineering aspects of distributed system characteristics, functionality and design goals; and
introduces a method of developing the engineering competencies required to design effective distributed,
networked military forces. 

 Fred M. Discenzo - Rockwell Automation

Managed Complexity in An Agent-based Vent Fan Control System Based on Dynamic Re-configuration

 New developments in advanced control techniques are occurring in parallel with advances in sensors,
algorithms, and architectures Dubbelsthat support next-generation condition-based maintenance systems.
The emergence of Multi-agent Systems in the Distributed Artificial Intelligence arena has shifted
control system research into a very challenging and complex domain. A multi-agent system approach enable
us to encapsulate the fundamental behavior of intelligent devices as autonomous components that exhibit
primitive attitudes to act on behalf of equipment or complex processes. Based on this approach, we have
implemented an initial set of systems that validate this methodology to manage the inherent complexity
of highly distributed systems to provide a consistent and aggregated system behavior. There are three
essential aspects of this new paradigm that suggest the significant potential of intelligent,
self-organizing distributed architectures; these are: · The complexity of solutions of highly monolithic
control systems is now understood as an aggregated family of well-delimited primitives. This aspect
exposes the new systems as hybrid solutions that area made of well-connected hardware and software
de-coupled units · The primitive components represent an emergent infinitesimal behavior in which each
primitive is only programmed with complete rules to satisfy local pursuits. Primitives are programmed
with incomplete (or none) rules to describe the relationship with other primitives. Each primitive uses
an agent language and is capable ofcommunicating peer primitives (e.g. Agents, Holons, or Cellular
Automata). · The system is then initially exposed to chaotic interactions among the primitives. The
chaotic interactions are defined as a highly intense capability matching task (association). It is also
postulated that the overall behavior will progressively converge to temporal clustering of primitives.
This is what we refer to as managed complexity. The concepts above and new engineering developments have
helped achieve new and important capabilities for integrating CBM technologies including diagnostics and
prognostics with predictive and compensating control techniques. Integrated prognostics and control
systems provide unique opportunities for optimizing system operation such as maximizing revenue
generated for capital assets, maximizing component lifetime, or minimizing total life-cycle costs. We
have defined and implemented an integrated diagnostic / prognostic / controller system in the context of
an agent-based representation. This integrated agent-based system has been implemented in a variable
frequency drive (VFD) and in a programmable logic controller (PLC). The VFD agent has been demonstrated
operating an axial vent fan system. During operation the system dynamically adjusts system operation
based on the collaboration with other agents operating in related parts of a chiller system. This paper
will describe the foundation technologies that are essential to realizing an adaptive, re-configurable
automation system. These technologies include diagnostic and prognostic algorithms, advanced control
techniques, agent-based framework for real-time automation systems, and system integration / modeling
techniques. We will describe a demonstration system we have developed for HVAC applications that utilize
these technologies. We will also cite the important, unprecedented capabilities this new system can
provide along with open issues and research challenges associated with the wide-scale deployment of
robust integrated prognostics and control systems in an agent framework. 

 Raj
P. Divakaran - Utah State University

Potential of Ant Colony Optimization to Satellite Image Classification

 Studies on the behavior of social insects such as ants have shown that they exhibit a collective
intelligence which cannot be explained based on the actions of the individuals that compose the colony.
In an ant colony, global or emergent patterns emerge due to the local interactions of the ants. This
ability to generate global patterns by a swarm of ants has served as the basis for the development of a
set of algorithms that emphasize distributedness, robustness and flexibility - Ant Colony Optimization
(ACO) Algorithms. Ant Colony Optimization techniques have been used to solve a number of network
problems such as the Travelling Salesmans Problem and the Job Scheduling Problem. Vitorino Ramos and
Filipe Almeida have shown that ACO algorithms could also be developed to solve the problem of image
segmentation. This development is of immense use to researchers in the field of image classification and
pattern recognition who have of late concentrated on developing robust, autonomous and flexible
algorithms that can delineate the classes with minimum or zero inputs from the user. The potential of
ACO algorithms to image classification has not been fully investigated so far. The purpose of this work
is therefore to determine if ACO algorithms can be successfully applied to the problem of image
classification of satellite imagery. 

 Brock Dubbels - The Norwegian
University of Science and Technology

Building a Network to the People

In consideration of the goals of the New England Complex Systems Institute to educate the population of
complex systems in awareness, perspective, and methods, it is important to consider ways to create a
learning web for the distribution of NECSI's most valuable resource: knowledge. Let it be made clear
form the beginning, beyond the knowledge and methods, the most valuable resource that this community has
is the individuals who are interested in Complex Systems work. These individuals could, through their
training at NECSI, become mentors and trainers to local community schools for a limited time. This might
enable NECSI to apply for educational dollars from local and federal sources and provide much needed
support for young people, broadening awareness of complex systems for young people, their teachers, and
a deeper learning for the students affiliated with NECSI in their training. The issue becomes one of
vision and method. In order to make this work, one might suggest that we use any methods possible, but
in consideration of the importance of practicing what we preach, it might be best to offer education
about complex systems through a complex systems approach. There is a wide variety of method in teaching
and learning, and many of the methods compliment if not endorse a complex systems approach to the world.
The goal of this paper is to put forward suggestions for teaching methodologies that are representative
of the complex systems approach to problem solving, i.e., it might be easy to have one person stand in
front of a group and lecture, but does this represent a movement away from centralized control or the
processes of group interaction. In the body of this paper, a review of approaches to curriculum and
learning promotion will be organized to present different modes of instruction from either a
traditional, or complex systems approach; recommendations will be made to offer an approach that not
only serves to educate the populace about complex systems, but also does so from a complex systems
approach in curriculum in form and substance. A review of the ideas of the K-16 education initiative and
the session on education at the ICCS3 conference will be included with current ideas in education and
learning. recommendations will be made for enacting this curriculum with members of NECSI, and future
members. 

 Juan Carlos Chimal Eguia - ESCOM-IPN

Some Further Analogies Between the Bak-Sneppen Model for Biologcal Evolution and the Spring-block
Earthquake Model

 Along recent years a lot of attention has been devoted to the so-called self-organized critical
systems: Which are open,extended systems, that organized themselves into steady metastable states,
without any temporal or spacial predominant scale (except those impose by the finite size of the
system). The SOC concept has been used to describe statistical properties of several physical systems by
means of numerical models based on cellular automata. In particular, Bak and Sneppen proposed a
SOC-model for biological evolution at the level of entire species or faunas, that exhibits punctuated
equilibrium behavior. On the other hand, Olami., Feder and Christensen, have suggested that the
two-dimensional spring-block earthquake model can explain some properties of real seismicity. In the
present work we show that there exists several interesting analogies between these SOC-models. Both of
them exhibit punctuated equilibrium in the long term, which leads us to suggest an equivalent
characterization of seismic and "evolutionary" provinces through the long term slopes of the
stair-shaped graphs of cumulative activity in the courseof time. 

 Ibrahim
Erdem - Yildiz Technical University

Customer Relationship Management in Banking Sector and A Model Design for Banking Performance
Enhancement

 Huge growth of Customer Relationship Management (CRM) is predicted in the banking sector over the next
few years. Banks are aiming to increase customer profitability with any customer retention. This paper
deals with the role of Customer Relationship Management in banking sector and the need for Customer
Relationship Management to increase customer value by using some analitycal methods in CRM applications.
CRM is a sound business strategy to identify the bank’s most profitable customers and prospects,
and devotes time and attention to expanding account relationships with those customers through
individualized marketing, repricing, discretionary decision making, and customized service-all delivered
through the various sales channels that the bank uses. In banking sector, relationship management could
be defined as having and acting upon deeper knowledge about the customer such as how to find the
customer, get to know the customer, keep in touch with the customer, ensure that the customer gets what
she/he wishes from service provider, and understand when they are not satisfied and might leave the
service provider and act accordingly. This study will underline CRM objectives such as growth, retention
and cost reduction. Increasing customers’ product cross-holdings, maximizing the contribution from
each customer through time and increasing efficiency are couple of those objectives, which this study
will cover. Under this case study, a campaign management in a bank is conducted using data mining tasks
such as dependency analysis, cluster profile analysis, concept description, deviation detection, and
data visualization. Crucial business decisions with this campaign are made by extracting valid,
previously unknown and ultimately comprehensible and actionable knowledge from large databases. The
model developed here answers what the different customer segments are, who more likely to respond to a
given offer is, which customers are the bank likely to lose, who most likely to default on credit cards
is, what the risk associated with this loan applicant is. Finally, a cluster profile analysis is used
for revealing the distinct characteristics of each cluster, and for modeling product propensity, which
should be implemented in order to increase the sales. 

 Peyman Faratin - MIT

A Multiagent Simulation Model of the Emergence and Dynamics of Negotiation in Complex Contracting Games

Complex systems are often characterized by the emergence of global behaviors that are often difficult to
anticipate simply by inspection of the system's components. In this paper we present some interesting
emergent properties that arise in the context of negotiation over contracts with multiple interdependent
issues. In particular, we show that a concessionary strategy is actually superior if adopted by both
parties, but the possibility of allowing agents to adopt a non-conessinary strategy introduces a
prisoner's dilemma. These results, derived from multi-agent system simulations, are potentially relevant
to human contexts such as collaborative design. 

 Timothy Field - Victoria
University of Wellington

Complex Adaptive Behaviour in Physical Simulation

 Continuing advances in computational power allows for the study of artificial life at a greater degree
of realism than ever before. We utilise this power to simulate physically realistic environments and
investigate the adaptation of virtual agents which optimize their behaviour in these environments with
respect to predefined goals. In particular, the performance and interaction of online reinforcement
learning and offline population-based learning algorithms in adapting both agent morphology and neural
network control is examined in simulations of rigid-body and fluid dynamics. We demonstrate examples in
which the tight coupling between an agent and the non-linear dynamical system it is situated in gives
rise to the emergence of non-trivial behaviour. 

 Brigitte Fleeman -
University of Texas at Austin

Sensemaking of a Change Intervention With Insights From Complexity Science

 Using Weick's (1995) framework of sensemaking and the insights gained from research on complex-adaptive
systems, I am interested in empirically investigating the question of whether and how differences among
agents affect the sensemaking processes of small groups as complex-adaptive systems. In general,
diversity is a requisite and hallmark of complex adaptive systems. In the organization literature,
diversity is seen more as a liability than an asset. On the one hand, diversity can hinder
organizational performance when multiple interpretations must be understood and different cues
integrated from a confusing array of possibilities. On the other hand, diversity often leads to
surprising and unique inputs giving rise to novel and comprehensive considerations. In applying these
perspectives to the interactions and relationships in diverse groups, an interesting tension emerges:
How can diverse inputs be used in group sensemaking? The focus of this research project is the influence
of functional diversity on the processes of sensemaking in small groups as complex adaptive systems and
the consequences in terms of group understandings. Using a naturalistic inquiry, the processes of
sensemaking in weekly operational meetings of a functionally diverse small manager group at a startup
site of a hospital network were followed for 7 months. The healthcare network had identified that
clinical professionals have difficulty to move into the responsibilities as managers. A volunteer unpaid
consultant developed an on the job trainingeffort and guided this leadership development project.
Although these managers had extensive clinical experience, business and financial experience was
limited. In addition to the 31 weekly operational meetings, several other meetings were observed and
audiotaped. Various interviews with managers and the volunteer consultant complement the group
sensemaking by following up on the individuals sensemaking. The data analysis is in progress. Specific
attention has been given to two surprising turn of events during the data collection: (1) an interesting
interpretation of the nursing managers with regard to the leadership development project and (2) the
surprising leaving of the group leader, the site manager. At the core are the different contributions to
topic streams and understandings during the group meetings by the managers from different backgrounds.
Insights from complexity science will contribute to the analysis and modeling of relationships and the
group sensemaking processes. For instance, ideas derived from Axelrod's (1997) modeling of dissemination
of culture can be applied to thinking about the development of ideas and crystallization of two or three
main topics; Kauffman's (1995) conflicting constraints in the NK model can serve as a source for
interpretation ideas regarding the connection to other hospital sites. Although these computer
simulations consider individual traits or connectivity, hierarchical influences (e.g., power) are not
included, which seem to be quite influential on the sensemaking of the group in this hospital setting.
Axelrod, R. (1997). The complexity of cooperation: Agent-based models of competition and collaboration.
Princeton, NJ: Princeton University Press. Kauffman, S. A. (1995). At home in the universe. New York,
NY: Oxford University Press. Weick, K. E. (1995). Sensemaking in organizations. Thousand Oaks, CA: Sage
Publications. 

 Peter A. Flemming - Vinyard Software

Understanding Patterns of Cyber Terrorism: An Evaluation Research Framework

 The proposed study develops an improved approach to understanding cyber terrorism by investigating the
complexities inherent in conventional terrorism. It begins by illustrating weaknesses in contemporary
research and develops a theoretical framework of international terrorism based on those variables which
are noted for having the greatest impact on influencing terrorist group behavior. In the first phase of
the study, emphasis is placed on establishing the role these variables play in shaping the dynamics of
terrorist events. In the second stage, the relationship between terrorist group structure and behavior
is reexamined to understand the nature of cyber terrorism. The overall research strategy incorporates
both qualitative and quantitative analyses as a means of furthering our understanding of terrorist group
behavior. A comparative analysis offers insight into how the characteristics of various terrorist groups
are reflected in their activity and how these are then reflected in cyber terrorist activity. An
extensive statistical analysis using ITERATE data that covers the 1968-2000 period is also employed.
This analysis performs a dual function of identifying/ highlighting the extent of international
terrorism over the last three decades and testing several hypotheses of terrorist group behavior in the
cyber environment. In sum, this study advances a research framework that identifies a key set of
elements that shape cyber terrorism. The comprehensive research strategy developed in this study
addresses inter alia a number of salient concerns. These are: 1) clarification of the importance of
terrorist group attributes in relation to conventional and cyber behavior; 2) empirical evidence that
refutes the many myths surrounding terrorism, its evolution and outcomes; 3) establishment of recognized
bench-marks of terrorist activity; and 4) identification of the cyber terrorist threat as it relates to
various classes of terrorist groups and their victims. In conclusion, this study offers both theorists
and policy makers the opportunity to reevaluate contemporary thinking on cyber terrorism and the initial
building blocks that are essential for future, informed research, as well as the amelioration of the
cyber terrorist threat. 

 Aaron Fritz - Utah State University

 Publication in the mainstream scientific literature is essential for the credibility and dispersal of
our research. Publication of agent-based simulation often presents special challenges. Some of these
challenges are because agent-based simulation is a new approach: convincing editors and reviewers that
research results are of general applicability, overcoming objections to models with too many parameters,
justifying selection of methods and inputs, satisfying the need for "validation". Other challenges are
more technical: how to adequately document a model within a journal article's size limitations, how to
present results derived from graphical interfaces. In this panel discussion, researchers who have
attempted and sometimes succeeded in publishing agent-based research will identify such challenges and
ways in which they can be overcome or circumvented. We hope to include the perspectives and advice of
journal editors who have managed the review of agent-based papers. 

 Philip
Galanter - New York University

On four modes of artistic engagement with complexity. Philip Galanter and Ellen Levy

 Scientific complexity is not the basis for an art movement or style. But just as the Copernican
revolution, the theory of evolution, and the innovations of Freudian psychology had implications beyond
science, we feel complexity compels reconsideration of traditional themes in art, criticism, and
philosophy. The author and artist Ellen Levy have organized a show called "Complexity" that will take
place at the Samuel Dorsky Museum in the fall of 2002 at SUNY New Paltz. In our research for this show
we have found that artists engage complexity on four modes: Portraiture - Artists can create realistic
presentations of natural complex phenomena that transcend typical scientific visualization, evoking both
a visual understanding and an emotive response in the viewer (e.g., Andreas Gursky and Harold Edgarton).
Descriptive Systems - Artists also experiment at various levels of conceptual abstraction. Artists will
often invent innovative, possibly idiosyncratic, systems, which describe complex phenomena in a way that
does not occur in the sciences (e.g., Mark Lombardi). Commentary - Just as artists have commented on
scientific and technical paradigms such as computers, genetics, and the like, they have also offered
critiques of physical and social systems (e.g. Hans Haacke). Technical Application - The study of
complexity offers a new rich toolbox for artists who create works via generative systems. Such
techniques include: genetic algorithms, swarming behavior, parallel computational agents, neural
networks, cellular automata, L-systems, chaos, fractals, a-life, and other forms of emergent behavior
(e.g., Karl Sims. John Simon Jr., and Woody and Steina Vasulka). This talk will include examples such as
the above and more, discuss the work and the ideas behind the work, and offer some speculative remarks
as to how notions from complexity may help to progress beyond the postmodernist dead-end that dominates
mainstream art criticism and theory today. 

 Auroop R Ganguly - MIT

Hybrid Statistical and Data Mining Approaches for Forecasting Complex Systems

 Complex natural and built systems typically exhibit limited predictability. However, even a marginal
increase in forecast accuracy often translates to significant benefits. Recent advances in data dictated
forecasting tools, the physics of natural and built systems, and the enabling information technologies,
have opened up new windows of opportunity. The modern forecaster has a more difficult task than her
predecessors, owing to the ever increasing complexity of built systems or the need to incorporate the
latest domain knowledge and measurements available from natural systems. However, she also has access to
more and better information. The challenge is to be able to efficiently assimilate and reconcile the
information to generate better forecasts. Approaches for data dictated forecasting of complex systems
could be categorized into two classes. The first approach could be called Traditional Statistics (TS),
which includes correlation and spectral analyses, Bayesian approaches, time series and regression models
like MARIMA, TAR and GARCH for the conditional mean and the variance, as well as methods for detection
of outliers, interventions, trends, causal indicators and seasonal or other pulses. These techniques are
statistically rigorous, but often need to make prior assumptions about the data or the underlying data
generation mechanisms. However, the domain knowledge needed to make the assumptions might not always be
readily available. The second approach could be termed Data Mining (DM), which include methods like
K-means, Gaussian mixtures, Artificial Neural Networks (ANN) and Decision Trees. DM sometimes lack
thorough statistical interpretations or the ability to weed out spurious trends, but might be able to
discern patterns that are not obvious to TS. This article hypothesizes that optimal hybrid forecasting
approaches could be designed that combines TS and DM tools, which could maximize the possibility of
discovering interesting patterns while retaining statistical rigor. Arguably the most commonly used TS
and DM tools for forecasting are MARIMA and ANN respectively. A simple forecasting algorithm is proposed
that attempts to combine these tools in an optimal fashion. The power of this new algorithm is
demonstrated using real data, through two example applications in disparate domains. The first example
is space time forecasting of high resolution rainfall amount. The governing equations of weather are
nonlinear and often exhibit chaos, while rainfall physics at high resolutions is not well understood and
exhibit high variability. Relevant data are available from remote sensors like radar and satellites,
ground measurements, and numerical weather m dels. Forecast improvements are rather difficult to obtain,
however even relatively small gain in accuracy could translate to reduced flood hazards. The second
example is sales and demand forecasting in the industry. The inherent variability in these systems are a
caused by human or extraneous factors, which are difficult to quantify. Data are available from
corporate data repositories, syndicated data providers, and third party sources. Dynamic markets and
changing corporate and competitive policies make the forecasting process complicated. However,
relatively small gains in the accuracy of demand forecasts could lead to better management of inventory,
and better tactical, operational and strategic plans. 

 Carlos Gershenson
- Univerity of Sussex

Complex Philosophy

 We present several philosophical ideas emerging from the studies of complex systems. We make a brief
introduction to the basic concepts of complex systems, for then defining abstraction levels. These are
useful for representing regularities in nature. We define absolute being (observer independent,
infinite) and relative being(observer dependent, finite), and notice the differences between them. We
draw issues on relative causality and absolute causality among abstraction levels. We also make
reflections on determinism. We reject the search for any absolute truth (because of their infinity), and
promote the idea that all comprehensible truths are relative, since they were created in finite
contexts. This leads us to suggest to search the less-incompleteness of ideas and contexts instead of
their truths. 

 Robert Ghanea-Hercock - BTexact Laboratories

Co-operative Agents in Network Defence

 The question addressed in this paper is how can complex information networks survive hostile attacks.
In particular we seek to understandsurvivability and defence in large-scale computing networks. An
integrated network of software agents has been constructed which provides adynamic immunological and
automated defence mechanism within a computing network. As recent evidence indicates, firms, governments
and otherorganisation urgently require better defensive strategies in cyberspace, (Anderson et al 1999,
and Briney 2000). In particular, the ability of anentity to maintain itself in the face of continuous
perturbation raises many issues related to metabolism, network topology, inter-agent bindingforces and
assimilation by external agencies. Using a collective formation of smart software agents we aimed to
create a form of adaptive immune-response structure within a computing network.Some preliminary work in
this field has already demonstrated the effectiveness of such methods using software agents, (Crosbie M.
& Spafford 1995,Filman & Linden 1996, Yialelis, Lupo & Sloman 1996). In order to investigate these
processes a multi-agent simulation model has been developed which demonstrates spontaneous group
formation and themaintenance of group integrity. These system aspects are proposed as integral
components of survivability. Each agent is susceptible to virusinfections, passed between each agent and
social assimilation by its local neighbours. From this model we observe a wide range of complex
socialbehaviours that could be selected from a few critical interaction parameters. We then introduced
an artificial immune system to each agent, whichallows learned 'antibody' solutions to be exchanged
between the agents within a social group. This mechanism reduced the infection level to asmall
percentage of the non-cooperative state. The interest in this behaviour stems from the concept that by
linking together the sensory and intelligence capabilities of a large number ofagents distributed across
a network we can amplify the ability of the network to resist attacks or intrusion. Specifically through
socialco-operation, agents can benefit from the combined defensive capabilities of their particular
group. The present global computing and communications network is a highly dynamic structure on an
immense scale. Future attempts to defend Intranet ortelecommunication networks will require equally
dynamic and adaptive processes. This work indicates that a cohesive network of sociallyinteracting
agents can create a highly robust and adaptive defence system for information networks. The agent
simulation we have developeddemonstrates that it is possible to create a population of autonomous
agents, which form self-healing social groups with greater resistance toattacks and perturbation than
isolated agents. 

 Zann Gill - RIACS, NASA Ames Research Center

Webtank

 Webtank (think tank on the web)and the management of organizational complexityIn this talk I'll give a
mini-tour through the webtank mock-up and discuss plans to use it to support think tank knowledge
management throughself-organization as the webtank evolves. I'll describe how this pilot experiment in
web-supported learning lays the foundation for a web-based "greenhouse" designed to address
thecomplexity of knowledge management as this interactive web environment scales up. The webtank will
document process events linked to an assessmentplan that can inform human/agent decisions about how to
modify the documentation strategy and guide website evolution. The operative principle of the webtank is
to encourage individual differences as the key to effective collaborative problem-solving andinnovation,
reflecting the principle of alife simulations where heterogeneous actors collaborate to solve problems.
As individual webtank modulesare uploaded, their location in the sitemap is fluid and is gradually
specified as they evolve their links to other entries. As the site graduallyscales up, the small number
of low connectivity links grows into a networked modular, structure that evolves toward increasing
coherence. 

 Willia Hardin Glover - Fielding Graduate Institute

An Exploratory Study of Key Factors of Self-Organization in Organizational Systems

 This research is an exploratory investigation of self-organization in complex organizational systems.
Self-organization is a process of transformation that culminates in the spontaneous structural
reorganization of complex social systems. An instrumental case study methodology was employed to explore
this social phenomenon in 2 Dallas-based organizations. A combination of archival data and interviews
revealed 2 important observations: a) Self-organizing behavior is inherent in organization
transformation and b) image, mission, and values, the organization's core elements are key factors that
contribute to self-organization in organizational systems. 

 Amrit Goel -
Syracuse University

Radial Basis Function Classification of Microarray Data Using Shin-Goel Algorithm. Amrit Goel and
Miyoung Shin

 Golub et.al. (1) introduced a generic approach to cancer classification based on gene expression
monitoring by DNA microarrays. Their class discovery procedure automatically distinguishes between two
types of cancer, acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), without class
knowledge. They state that it should be feasible to develop cancer classifiers based solely on gene
expression monitoring independent of previous biological knowledge. Here, we develop radial basis
function (RBF) classifiers for distinguishing between ALL and AML based on gene expressions. RBF
classifiers have been used in a wide range of disciplines from engineering to medical sciences and
economics to astronomy. However, current algorithms for determining RBF model tend to produce
inconsistent designs due to their ad-hoc nature. Recently, Shin and Goel (2,3) introduced a new approach
for the design and analysis of radial basis function classification models. In general, there exists a
conflict between model complexity and performance, the so-called bias-variance dilemma. The Shin-Goel
(SG) algorithm selects the lowest complexity model with the "best" compromise between training and test
errors via a user-specified complexity control parameter. Their approach provides an objective and
systematic design methodology due to its origins in the mathematical properties of the interpolation and
design matrices associated with the RBF model. This algorithm involves no randomness in classifier
development and can be almost totally automated. Finally, the SG algorithm is computationally fast since
it involves matrix computations as opposed to iterative search employed in current algorithms. In this
paper we use the SG algorithm to develop Gaussian radial basis function classifiers for the microarray
data of Golub et.al.(1). We consider two data sets. The first uses 7129 and the second 50 gene
expressions. In both sets, 38 patients are used for classification model development and 34 for testing.
Our results are summarized below. 7129 gene data set For training data of 38 patients, the SG algorithm
produced an RBF design with twenty nine Gaussian basis functions and classified all patients correctly.
For test data, 29 of the 34 patients were classified correctly by the RBF classifier. 50 gene data set
For training data, the SG algorithm produced an RBF classifier with five Gaussian basis functions in the
hidden layer and classified all 38 patients correctly. For test data, 33 of the 34 patients were
correctly classified by the RBF classifier. In this paper we also report on the sensitivity analyses of
the developed classifiers. These analyses show that the training and test errors for both data sets are
relatively robust to global basis function width as well as to model complexity (number of basis
functions). REFERENCES [1] T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov,
H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, E. S. Lander. Molecular
Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science,
286:531-537, 1999. [2] M.Shin and A. L. Goel. Empirical Data Modeling in Software Engineering Using
Radial Basis Functions. IEEE Transactions on Software Engineering, 26:567-576, 2000. [3] M. Shin, and A.
L. Goel. Radial Basis Function Model Development and Analysis Using the SG Algorithm (Revised),
Technical Report, Department of Electrical and Computer Science, Syracuse University, Syracuse, NY,
2002. 

 Noah C. Goldstein - UCSB Geography

Co-evolution in Coupled Human-Natural Systems

 Coupled human-natural systems interact in many dimensions, including the cultural, the physical, and
the biological. Over time, coupled human-natural systems interact and develop to exhibit coevolved
behaviors. These behaviors are promoted by feedbacks between the two systems as well as the dynamics
inherent to each individual system. Many inter-system feedbacks occur in cycles, based on biophysical
drivers or cultural characteristics. This paper has two goals. First, this paper will present the
behavioral relationship of a coupled human-natural system and discuss if it can indeed be called one of
"coevolution". This is challenging, as different types of evolution are at play in each individual
system; Human systems by cultural (Lamarckian) evolution, Natural systems by both biological (Darwinian)
evolution, and physical evolution of the landscape. The coupled system complex provides a novel locus of
coevolution, one that can be challenging to understand and describe. Comparisons to purely biological
examples will be made and a definition of coevolution for complex systems will be proposed. Second, this
paper will describe examples of coupled human-natural systems, and characteristic behaviors that make
them unique and similar. These include agriculture as well as river flooding and other natural
disasters. What is important in examining specific coupled human-natural systems are historical events
and trends that can then be used to understand where the systems are linked, and over which spatial and
temporal scales the linkages occur. A case study of urban growth and wildfires in Santa Barbara,
California will be used as an example. The coastal city is the home to a growing population, along with
an extensive urban-wildland interface with the fire-adapted chaparral of the Los Padres National Forest.
In recent decades, wildfires have caused millions of dollars of damage to homes and structures within
the urban fabric. In addition people have contributed to large alterations in fire regime, and fire
spread. Recent developments in the historical and forecasting simulation modeling of the urban-wildfire
coevolution in Santa Barbara will be presented along with a framework to study the potential risks that
the growing urban area may foresee in the future. 

 Robert L. Goldstone -
Indiana University

The Allocation of Agents to Resources in a Networked Multi-Player Environment. Robert L. Goldstone &
Benjamin Ashpole

 Our goal in this research is to collect a large volume of time-evolving data from a system composed of
human agents vying for resources in a common environment, with the eventual aim of guiding the
development of computational models of human resource allocation. We have developed an experimental
platform that allows a large number (more than 30) of human participants to interact in real-time within
a common virtual world. We recorded the instant-by-instant actions of each individual within this
environment. Two resource pools were created with different rates of replenishment. The participants'
task was to obtain as many resource tokens as possible during an experiment. An agent obtained a token
by being the first to move on top of it. In addition to varying the relative replenishment rate for the
two resources (50-50, 65-35, 80-20), we manipulated whether agents could see each other and the entire
food distribution, or had their vision restricted to food in their own location. As a collective, the
agents would optimally harvest the resources if they distribute themselves proportionally to the
distribution of resources. Several empirical violations of global optimality were found in the 8
sessions of 20+ participants that were tested. One observed suboptimality was an underutilization of
resources that occurred because of frequent pool-switching by individual agents. Agents did not mimic
the distribution of agents' resource allocations by their individual distribution of allocations.
Nevertheless, they did alternate resources frequently enough to introduce collective inefficiencies in
harvesting. Second, there was a systematic underutilization of the more preponderant resource. For
example, agents distributed themselves approximately 70% and 30% to resources pools that had relative
replenishment rates of 80% and 20%, respectively. The expected pay-off per agent was larger for pools
with relatively high replenishment rates. Third, there were oscillations in the harvesting rates of the
resources across time. Perceived underutilization of a resource resulted in an influx of agents to that
resource. This sudden influx, in turn, resulted in a glut of agents, which then led to a trend for
agents to depart from the resource region. This cyclic activity in the collective data was revealed by a
Fourier analysis showing prominent power in the range of 30 seconds per cycle. Fourth, agents were more
dispersed within a resource pool than optimal. The distributions of both agents and resources were well
fit by Gaussian functions, and the means of the agent and resource distributions closely matched.
However, the variances for the agent distributions were much larger than for the resource distributions,
particularly when agents' vision was restricted and for agents in pools with relatively low
replenishment rates. These results are discussed in terms of optimal foraging behavior, K-armed bandit
problems, frequency dependent selection, and resource allocation strategies. In addition to providing
practical suggestions on how to improve the utilization rate of a set of resources by a decentralized
collection of agents, the current data highly constrain models of agent and resource allocation. 

 Irina I. Grichtchenko - Yale University

The Electrogenic Na/HCO3 Cotransporter Carries CO=3: Evidence From Surface-pH Measurements in XenopUs
Oocytes Co-Expressing NBCe1 andCAIV. I. I. Grichtchenko, and W. F. Boron

 An approach for clarifying whether the electrogenic Na/HCO3 cotransporter (NBCe1) transports HCO3- or
CO3= was suggested by Grichtchenko & Chesler (Neurosci 62:1057, 1994), who monitored pHo in hippocampal
slices. Here, we used a blunt electrode pushed up against the oocyte membrane to monitor surface pH
(pHS) in voltage-clamped oocytes coexpressing NBCe1 and CAIV (extracellular, GPI-linked carbonic
anhydrase). Stepping the holding potential (Vh) from 50 to 0 mV produced an outward current (deltaINBC =
558+-49 nA; n=4), and surface acidification (delta pHS = 0.042+-0.006). Stepping from 50 to 100 mV
produced an inward current (deila INBC = 453+-41 nA; n=4) and surface alkalinization (delat
pHS=0.040+-0.005). In control oocytes expressing CAIV and ENaC (not NBCe1), we observed a 1500-nA
current but no delta pHS. If NBCe1 transported only HCO, inhibiting CAIV with 600 uM acetazolamide (ACZ)
should decrease the magnitudes of delta pHS values. However, we found that ACZ increased the magnitudes
by ~75%, consistent with the following model: two HCO3 ions approach the unstirred layer near NBCe1. One
dissociates to form H+ and the CO=3 that NBCe1 transports. The other HCO3 neutralizes the H+ to form CO2
+ H2O, catalyzed by CAIV. 

 Dominique Gross - Dublin City University

 Currently the science of complexity lacks a generally accepted formal definition of complexity.
However, especially among modellers of complex natural systems, the notion of CAS slowly seems to emerge
as a widely adopted working definition of complexity. We want to suggest that the notion of CAS lacks at
least two crucial features in order to suit as a framework for understanding real complex systems. These
features are "radical openness" and "contextuality". The former describes the property of real complex
systems to lack clear boundaries and the latter describes the fact that components of real complex
systems typically fulfill multiple functions simultaneously. 

 Vladimir
Gudkov - University of South Carolina

Multidimensional Network Monitoring for Intrusion Detection

 An approach for real-time network monitoring in terms of numerical time-dependant functions of protocol
parameters is suggested. Applying complex systems theory for information flow analysis of networks, the
information traffic is described as a trajectory in multi-dimensional parameter-time space with about
10-12 dimensions. The network traffic description is synthesized by applying methods of theoretical
physics and complex system theory, to provide a robust approach for network monitoring that detects
known intrusions, and supports developing real systems for detection of unknown intrusions. The methods
of data analysis and pattern recognition presented are the basis of a technology study for an automatic
intrusion detection system that detects the attack in the reconnaissance stage. 

 George J. Gumerman - University of Arizona & Santa Fe Institute

Evolving Social Complexity in the Prehistoric American Southwest. George J. Gumerman , Alan Swedlund ,
Jeffery S. Dean ,Joshua Epstein , and Robert Axtell

 The Anasazi (ancestors of the present day Pueblo peoples) of the American four corners area were a
technologically simple agricultural society, dependent on maize agriculture. Ethnographic, historical,
and archaeological accounts document a culture with complex social, religious, and economic systems.
Detailed reconstruction of the past environment from A.D. 200 to A.D. 1450 permits an understanding of
the coupled natural and cultural landscape of the region. Changes in Anasazi social complexity are
closely linked with perturbations in environmental conditions. An agent-based model has been developed
in order to test the relationship of a number of physical and cultural attributes to changes in Anasazi
social complexity. The changing landscape of Long House Valley in northern Arizona and the location and
size of prehistoric settlements on an annual basis has been digitized. In contrast to the actual
cultural situation, agents, or artificial Anasazi, evolve on the same digitized landscape according to
rules derived from the ethnographic and historic data and inferences from archaeological research. The
artificial Anasazi are then compared to the actual situation to determine if inferences made about the
role of demography and environment in the changing social complexity are correct. The modeling effort
has demonstrated that nutritional, demographic, and environmental conditions have had a greater impact
on the Anasazi evolutionary trajectory than previously suspected. In addition, the models have indicated
that certain strategies that were not implemented by the Anasazi might have buffered the society from
collapsing into simpler forms and even permitted them to stay in the area they abandoned at
approximately A.D. 1300. The great environmental and cultural detail and the relatively simple
technological society of the Anasazi make the four corners region of the southwest an excellent natural
laboratory in which to test hypotheses about evolving social complexity. 1 Arizona State Museum,
University of Arizona, Santa Fe Institute 2 University of Massachusetts - Amherst 3 Arizona State
Museum, University of Arizona 4 Brookings Institution, Santa Fe Institute 5 Brookings Institution 

 Ivar Hagendoorn

Emergent Patterns in Dance Improvisation How Complexity Theory Inspires Choreography and Vice Versa

 In a traditional choreography a choreographer determines the motions of a dancer or a group of dancers.
Information theory shows that there is a limit on the complexity that can be created in a given amount
of time. This is true even when building on previous work, since movements and their interactions have
to be communicated to the dancers. When creating a group work, choreographers circumvent this problem by
either focusing on the movements of individual dancers (giving rise to intricate movements but within a
simple spatiotemporal organization) or on the overall structure (intricate patterns but simple
movements). Complexity theory offers a different paradigm towards the generation of enticing patterns.
Flocks of birds or schools of fish are generally considered Œbeautiful¹ but lack a central governing
agent. Computer simulations of individual based models show that a few simple rules can give rise to the
emergence of the kind of patterns seen in flocks or swarms. In these models individual agents are
represented by dots or equivalent shapes. For this and other reasons, which will be discussed, to be of
use to choreography and to be implemented on or rather with dancers, some additional rules will have to
be introduced. I will present a number of possible rules, which emerged from Œreal life¹experiments with
dancers and the considerations that shaped them. I will also extend the individual based model
framework, which is based on local interactions between single agents, to include the interaction with a
group of agents acting as a single agent. Dancers may perceive the global structure they form, e.g. a
line or a cluster, and then put that knowledge to creative use according to some pre-established rules,
e.g. if there is a line, form a circle or if there is a cluster spread out in all directions. Some of
the rules presented here may be applied back to other complex systems. The present paper is also an
invitation to complexity theorists working in different fields to contribute additional rules and ideas.


 Jennifer Hallinan - University of Queensland

Iterative Diffusion of Vectors for the Detection of Modularity in Complex Networks

 Systems biology offers the prospect of new insights into the emergentproperties of complex biological
systems such as cells, tissues, whole organisms and ecosystems. However, the data which has been
collected to date is incomplete, raising concerns that attempts to model biological systems at a systems
level are premature. We contend that incomplete data sets are not necessarily a problem if the data
which does exist is organized into structural modules characterized by relatively high connectivity
within the nodule and lower connectivity between modules. Modularity appears to be widespread in
biological systems ranging from subcellular networks to ecosystems, and is important to both the
functionality and the evolution of the system concerned. The ability to identify modules within largely
uncharacterized biological networks would be valuable in several ways. It would assist with the
characterization of uninvestigated nodes based upon their module membership and it would permit
assessment of the extent to which analysis of the already characterized nodes of an incompletely studied
network is feasible. If the well understood nodes form a largely independent module within the network,
the fact that much of the rest of the network is uncharacterized becomes less relevant. In this paper we
describe an algorithm for the objective identification of modules within a network.. Each node of the
network is assigned a binary vector n bits long, where n is the number of nodes in the network. The
initial vector for node i consists of a 1 in position i and 0 in every other position. The system then
undergoes an iterative process of vector modification, as follows: .At each time step an edge in the
graph is selected at random..The vectors representing the nodes at each end of the edge are compared.At
any position i in the vector at which the entry is not equal to 0 an amount delta is added to the larger
of the two entries and subtracted from the smaller This process is iterated until each node in the
network has been modified on average p times, where p is a tunable parameter of the system. The final
set of vectors is then subjected to a standard clustering method (we have used k-means and Kohonen's
Self-OrganizingMap) in order to assign cluster membership to each node in the network.. Each cluster can
be interpreted as a structural module within the original network. Using artificially generated networks
composed of modules of known degree of clustering, the iterative vector diffusion algorithm performs
robustly. We have also applied the algorithm to the network of protein-protein interactions in the yeast
Saccharomyces cerevisiae.In this network the nodes are proteins and the links are protein-protein
interactions detected using a yeast two-hybrid screen. Details of the nature, function and subcellular
location of the protein are unknown for more than half of the proteins in S. cerevisiae. The
characterization of the modules detected, and the potential utility of the IVD algorithm for the
detection of functional modules in a network such as this is discussed. 


Zhangang Han - Beijing Normal University

Evolution of Labor Division For Cooperative Agents With Learning Ability

 This paper studies the dynamics of how cooperation in a multi-agent system is evolved. A multi-agent
system usually has many self-interested individuals; each makes decisions based on environment and their
own states at the current time. Information is usually localized, and the ability for each individual to
move is confined. The evolution of the multi-agent system may lead to collective behaviors due to mutual
interactions among individuals. Specialization increases productivity in an economy that will result in
labor division. This paper simulates a specialization evolution process by introducing one kind of
agents that each can take two kinds of tasks: search for resources and mine the already found resources.
Agents search resources by random walk. An agent can decide whether to mine the resource found by itself
or trade the resource to other agents nearby through a market-like mechanism. There are no global
planning mechanisms. The agentssearching and mining capabilities can be adjusted through
learning-by-doing with maximum value constrains for the capabilities. An agent's specialization degree
is measured by the ratio of profits made by searching to all the profits made. Initially, agents are
evenly partitioned in the specialization degree space. Evolution leads to a pattern that agents tend to
specialize in their works. There are some of the agents make benefits only through searching for
resources and trading them to others and some of the agents make benefits only through accepting trades
from others and mining the resources. Only through cooperation, can the individuals that specialize in
searching survive. This paper also studies the effect of parameters on division of labor, as trading
range, trading price, searching range, mining speed, population density, and resource density. At
certain areas of the parameter space division of labor does not emerge. This paper locates the phase
transition point. 

 Steven Hassan - Freedom of Mind Resource Center Inc.

A Complex Systems Approach to Countering Brainwashing, Mind Control, and Terrorism

 Destructive mind control is a systematic social influence process that typically includes deception,
hypnosis and behavior modification techniques to subvert an individual1s identity in order to create a
new pseudo-identity in the image of the leader. A mind control model will be presented that demonstrates
how the control of: behavior; information; thoughts and emotions are used by destructive cults (pyramid
structured, authoritarian regimes) to make cloned identities, obedient and dependent to its authority.
Furthermore, the presentation will include how a complex system model called the Strategic Interaction
Approach can be used to mobilize social networks to empower impacted individuals to reassert their own
identity and independence and break free from the pseudo-identity. http://www.freedomofmind.com 

 Yu-Chi Ho - Harvard

The No-Free-Lunch Theorem, Complexity and Computer Security

 Using a simple explanation of the No Free Lunch Theorem and the reasonable assumption that P(NP, we
derive certain general conclusions regarding the limiting behavior of complex systems and network
security. 

 Guy A. Hoelzer - University of Nevada Reno

On the Relationship Between Natural Selection and Self-Organization

 Biological evolution is often cited as an example of self-organization in a dynamical complex system,
which is consistent with the notion that self-organization continually optimizes structures in response
to unpredictable perturbations. Some authors have even suggested that this model of adaptive evolution
should be considered as an alternative to Darwin's model of adapation through natural selection. There
is, however, no reason to think that these are mutually exclusive processes. In this talk, life will be
described as a self-organized engine that reduces an energy gradient created by the earths shadow; the
constraints of birth/reproduction/death increase the "coarseness of the grains" of the system, permit
better optimization of energy flow, and provide the foundation for natural selection among phenotypic
variants to occur. This self-organizing engine can then take advantage of natural selection as a means
of further optimizing energy flow. Indeed, data recently compiled by Brown, West and colleagues show
strong and predictable relationships between energy processing and fitness in many taxanomic groups. In
this view, natural selection is both distinct from the general self-organizing process and used as a
tool for self-organization, thus placing Darwin's theory into a more general framework. 

 Craig vanHorne - Harvard Medical School

The Principles of Connectivity, Self-organized Criticality, and Complex Adaptive Systems May Further Our
Understanding of the Symptoms and Treatment of Parkinson's Disease

 Parkinson's disease has been identified as a progressive loss of dopaminergic neurons within the
substantia nigra, a small nucleus deep within the midbrain. The symptoms of the disease, tremor,
rigidity and hypokinesia, begin to develop once there has been a loss of 70-80% of this neuronal
population and worsen to an incapacitating condition as the degeneration continues. Two substantial
issues regarding the disease are that the underlying etiology of the dopaminergic cell loss remains
unknown and that despite the availability of medications and procedures that provide temporary relief of
symptoms there are no treatments or interventions that halt the progressive neuronal death. As the
disease progresses, the treatments lose efficacy and are often complicated by disabling side effects. A
potential limitation has been the consideration of the disease as being due to either a loss of dopamine
or to the loss of the cells themselves. While both play a role, I propose that it may In terms of
complex adaptive systems and self-organized criticality there are several assertions. It is reasonable
to identify the basal ganglia as a complex adaptive system comprised of several sub-cortical nuclei
composed of highly interconnected neuronal populations. The connections allow for multiple areas of
input, output, and feedback loops. In addition, there are functional and structural features that allow
for adaptive changes to occur in response to external and internal changes. The basal ganglia may also
be considered to be a self-organized system demonstrating a dynamic function that is positioned in the
sub-critical state at the edge of chaos. In this sense, the critical state represents the flow and
propagation of signaling through and within the interconnected nuclei. This dynamic function allows
smooth and accurate motor activity to occur at the level of the organism. In this system the underlying
feature of self-organized criticality is the many degrees of free 

 Michael
Howard - HRL Laboratories

Amorphous Predictive Nets. Michael Howard, Regina Estkowski and David Payton

 This paper describes our work on the development of biologically inspired approaches to achieve
coordinated action from extremely large numbers of distributed, loosely connected, embedded computing
elements. In such networks, centralized control and information processing is impractical. If control
and processing can be decentralized, the communications bottleneck is removed and the system can become
more robust. Since conventional computing paradigms provide limited insight into such decentralized
control, we look to biology for inspiration. With the recent progress in the miniaturization of sensors
and computing elements and in the development of necessary power sources, large arrays of networked
wireless sensor elements may soon be realizable. The challenge will be to develop software that enables
such amorphous arrays to self-organize in ways that enable the sensing capabilities of the whole to
exceed that of any individual sensor. Our goal has been to devise local rules of interaction that cause
useful computational structures to emerge out of an otherwise amorphous array of distributed sensor
nodes. These distributed logical structures appear in the form of local differences in sensor node
function, and local differences in node-to-node connectivity. These local differences serve to form
distributed circuits among nodes that allow a group of nodes to perform cooperative sensing and
computing functions that are not possible at any single node. Further, since the local differences
emerge and are not pre-programmed, there is never a need to assign specific functions to specific nodes.
We can start out with a completely amorphous array of sensor nodes, running the same software, and have
them all automatically differentiate into the necessary computational structures. In this paper, we
describe two methods, each using only local interactions between nodes, to detect the presence and
heading of an intruder moving through a distributed sensor network. In one method, nodes do not
differentiate, but they rely on temporal derivatives of signals produced by neighboring sensors. While
this method is capable of detecting motion, it cannot discern the direction of motion within less than a
180-degree cone. In the second method, nodes differentiate along parallel spatial bands. This results in
a pattern state within individual nodes that either sensitizes or desensitizes the nodes to particular
activation/inhibition signals from neighboring nodes. Activation/inhibition rules are designed such that
messages signaling the presence of an intruder are inhibited along bands of the same type, but are
propagated into bands of a different type. This, in effect, leads to a form of moving edge detection for
objects moving across the sensor array from one spatial band to another. These methods provide a purely
distributed means of computing the direction and likely destination of a sensed movement, with no need
for centralized data analysis or explicit sensor data fusion. As with any approach to amorphous
computing, it is impractical to try to extract information from the network, such as a global map of
node activation. We present options for exploiting the results of the distributed computation performed
by the network. 

 Sui Huang - Children's Hospital, Harvard Medical School

Gene network topology and dynamics in mammalian cell fate regulation

 The advent of large-scale genomic technology opens a new window to the old riddle of genome-to-phenome
mapping. A first step towards an integrative ?bottom-up? understanding of living systems based on
genomic information is to study as an entity the network of interactions between the genes and proteins.
Recent work has revealed interesting characteristics of the topology of genomic
(gene/protein/metabolite) interaction networks, such as the power-law distribution of connectivity
between the genes. However, it remains largely unknown how the global network topology translates into
?emergent? phenotypic behavior. A first level of emergence in the multi-level, hierarchical organization
of living system is the global cell behavior which can bee seen as the ?macroscopic biological
observable? that is determined by the underlying gene regulatory network, the ?microscopic? level. Cell
fate regulation, i.e. the switch between a finite set of discrete phenotypic cell states, such as
proliferation, differentiation and apoptosis, represents such a robust, emergent cell behavior. First,
we used conventional cell biology experiments to measure cell state transition dynamics in human cells
and show that at the macro-level cell fate dynamics is compatible with the idea that cell
differentiation states are attractor states, as proposed by Kauffman and others. We then used DNA
microarray-based dynamic gene expression profiling to analyze a cell fate switch and present preliminary
data demonstrating that also at the micro-level the notion of cell fates being attractor states of the
high-dimensional system of interacting genes is consistent with experiments. These findings suggest that
the observed dynamics of cell behavior is a direct manifestation rather of the structure of the state
space (e.g. phase singularities) of the network rather than of its topology. Therefore, we studied how
topological features found in real gene networks affects the dynamics, i.e., the ?attractor landscape?
in simulated, generic networks. We show that power-law distribution of connectivity, a topology found in
all cellular networks studied, exhibits a state space structure that might be more favorable for
biologically meaningful cell fate regulation. Implications of this finding for fundamental biological
properties of cells, such as the coexistence of stability and flexibility (adaptation), as well as
applications in cancer research and drug discovery are discussed. 

 Alfred W.
Hubler - University of Illinois

Experimental Appoaches to Complex Systems. Alfred W. Hubler and Paul Melby

 We study adaptation to the edge of chaos in high dimensional experimental system. We investigate
experimentally high dimensional Chua circuits with low pass filtered feedback. We find that Chua
oscillators adapt to edge of periodic regimes, which a larger than a certain minimum size. The minimum
size of the period windows depends on the size of the feedback. We also find that in the chaotic regime,
adaptive Chua circuts with external control tend to disentrain from the target dynamics and tend to
adapt to the edge of chaos. These finding may explain why chaos is rarely observed in complicted
chemical systems and other complicated systems. 

 Tim Huerta - University of
Southern California Los Angeles

Complexity as a unifying paradigm for Organization and Management

 The literature on the philosophy of science is undergoing a transformational change that challenges
academics with the inadequacies of the positivist perspective. The traditional focus on empirically
identifiable linear relationships and an historical foundation based on idealized associations have
misconstrued simplification as truth. Critical social scientists and post-modernists claim there is no
hope for generalizing our understanding of social phenomena, and phenomenologists claim there is no
expectation that such an understanding of organizations will ever develop. Yet complexity theory may
offer an effective paradigm to handle this seeming contradiction between the advocates of the
rational/empirical perspective and the challenges of post-positivists. In short, the complexity movement
in the social sciences has the potential to account for all of these perspectives in a holistic and
unifying theoretical framework that embraces the seeming paradox through a deeper understanding of
causal networks. Philosophical arguments made by phenomenologists, critical social scientists, and
postmodern academicians offer a number of critiques of positivism. These post-positivist challenges
have, in part, come from the arguments articulated at the beginning of the quantum movement in physics.
Rather than offer a foundation for future scientific discourse, however, these critiques have only
offered a patchwork perspective with future promises of greater understanding. Fox and Miller's
discourse theory, for example, provides no basis for inquiry consistent with the scientific method. Tom
Cook's Quasi-Experimentation attempts to use a traditional empirical paradigm, but then argues that
positivist research is impossible because academics can't distinguish between a causal or corollary
relationship. By calling positivists epistemologically bankrupt, these perspectives have destroyed any
basis for generalizable academic research. Complexity theory offers a means to address the challenges
posed by these competing perspectives and thereby provides a basis for epistemological discourse in the
study of organizations. Complexity acknowledges the role of empirical research and embraces parsimony
ideologically situated with traditional positivism while at the same time provides a mathematical
framework for understanding complex behaviors. At the same time, it recognizes non-linear and recursive
causality, sensitivity to initial conditions, and the importance of context implicit in the
post-positivist arguments. By affirming both perspectives, complexity has been used to leverage a
greater understanding of why simplifications may not adequately describe interactions, while at the same
time allowing for the construction of general theory to describe interactions. In essence, complexity
has the potential to become an interdisciplinary paradigm that addresses the concerns of both sides of
the epistemological debate. Complexity may constitute a unifying paradigm to address simultaneously the
concerns of the post-positivist in the empirical framework of the positivist consistent with traditional
positivist philosophy. This paper identifies a roadmap to understanding social systems and their
organization by using complexity science's reconciliation potential. By revisiting both sides of the
epistemological debate between positivist and post-positivist perspectives, complexity is identified as
a suitable candidate for unifying the underlying philosophy of social inquiry. The implications for
organization of a complexity perspective are also identified. 

 Michael J.
Jacobson - The Distributed Learning Workshop

Complex Systems in Education: Integrative Conceptual Tools and Techniques for Understanding the
Education System Itself

 This session will discuss the results of planning meetings held as part of a New England Complex
Systems Institute project on Complex Systems and K-16 Education that was funded by the National Science
Foundation. The project involved a diverse group of scientists (physicists, chemists, biologists,
psychologists, sociologists, mathematicians, computer scientists) and educational researchers in
meetings to consider common ground that could be used to generate researchable ideas for integrating the
field of educational research with advances in the study of complex systems in other disciplines. The
session will provide an overview of ideas that were generated in the meetings in areas such as the
potential implications of complex systems for education in terms of content, teaching, learning and
cognition, and for understanding from a fresh perspective the complex system of education itself. The
final report and working papers are available at: http://necsi.org/events/cxedk16/cxedk16.html. 

 Klaus Jaffe - Instituto Venezolano de Investigaciones Científicas

On the Modulation of Variance in the Evolution of Complex Systems: Sex in Artificial Life

 Using computer simulations I studied the conditions under which diverse degrees of ploidy, mutation
rates, hermaphroditism and sex (recombination) were evolutionary stable. The parameters that showed
relevance to the stability of sex were: variable environments, mutation rates, ploidy, number of loci
subject to evolution, mate selection strategy and reproductive systems. The simulations showed that
mutants for sex and recombination are evolutionarily stable, displacing alleles for monosexuality in
diploid populations mating assortatively when four conditions were fulfilled simultaneously: selection
pressure was variable, mate selection was not random, ploydy was two or the reproductive strategy was
haplo-dipoid or hermaphroditic, and the complexity of the genome was large (more than 4 loci suffered
adaptation). The results suggest that at least three phenomena, related to sex, have convergent adaptive
values: Diploidy, sexual reproduction (recombination) and the segregation of sexes. The results suggest
that the emergence of sex had to be preceded by the emergence of diploid monosexual organisms and
provide a explanation for the emergence and maintenance of sex among diploids and for the scarcity of
sex among haploid organisms. The divergence of the evolutionary adaptation of the sexes is a derived
consequence of the emergence of sex. The simulation results allow to postulate a taxonomy of mechanisms
regulation 

 Sanjay Jain - University of Delhi, Indian Institute of Science, and
Santa Fe Institute

Emergence, Growth and Collapse of Cooperative Organizational Structure in Evolving Networks

 A mathematical model of an evolving network, motivated by the origin of life problem, will be
discussed. The evolution exhibits an initial phase of no cooperation until a small cooperative
structure, an autocatalytic set, appears by chance. Because of its cooperative property and consequent
stability, it turns out that evolution gets the opportunity to build upon this structure, and the
autocatalytic set expands. This is the phase in which cooperation emerges and grows. The success of this
organization and its dominance of the environment leads eventually to the emergence of a new kind of
competition among its members. This can cause the robust cooperative organization to become fragile and
collapse. The underlying mechanisms and time scales for these processes will be discussed. 

 James Holland Jones - University of Washington

 Compartmental models for the transmission of infectious diseases have become part of the standard
toolkit in epidemiology. However, assumptions relating to proportional mixing employed in these models
severely limit their applicability with respect tosexually-transmitted diseases. Several methods have
been developed torelax the assumption of proportional mixing. I use a loglinearmodeling approach to
estimate (nonproportional) mixing preferencesfrom clinic data to create a compartmental model for
Chlamydiadynamics in Seattle/King County. Such clinic data are clearly biasedin terms of their
representation of a number of important featuresincluding race, sex, and age distribution. Using a
Bayesian model, Iextend the loglinear modeling framework of Morris (1991) so that I caninclude prior
information on the structure of the population whichfeeds the clinic. This framework also proves useful
for incorporatinguncertainty into estimates of biological parameters in the model. Ishow that race- and
sex-differentials in prevalence can arise simplyas a function of (1) differential mixing as a function
of race andsex, and (2) different latent periods in men and women. Following theapproach of Raftery and
Poole (2000), I reconcile uncertainty withmodel inputs with the observed (deterministic) model outputs,
andcalculate posterior predictive distributions for both biological andsocial parameters which can then
be confronted with additional datacollected in this ongoing work. 

 Keith Josef
- Syracuse University

Laboratory Controlled Model System for Study of Complexity Models that Apply to the Signaling Network of
a Single Biological Cell. K. Josef, J. Saranak, And K. W. Foster

 The unicellular alga Chlamydomonas is a free-living multi-input multi-output nonlinear signal
transduction network. This organism is 10 mm in diameter with two anterior cilia, each 15 mm in length
and 0.24 mm in diameter. The cell has an ?eye? with a rhodopsin photoreceptor. The cell also has
chlorophyll-based photoreceptors, flavoproteins, a phytochrome (730 nm) receptor, and probably other
photoreceptors.This alga swims about 120 mm/s with the cilia beating (20-80 Hz) in a breaststroke .The
stroke frequency is modulated by changes in external environmental factors such as light levels,
temperature, and chemical composition of the immersing medium. The cells also rotate at 2 Hz, scanning
their environment producing a signal for tracking light sources. Based on multiple environmental inputs
the cell decides whether to go towards, away or orthogonal to the light direction, presumably to
optimize its survival. We specifically study the real-time events of the signaling network with emphasis
on the pathway from rhodopsin to ciliary motion. An electro-optical method monitors the cell?s ciliary
beating. Suctioned onto the tip of a micropipette, the cell?s magnified image is focused onto a quadrant
photodiode array. Two independently-random light patterns stimulate the cell and the variations in light
scattered from the two cilia passing into and out of each photodiode quadrant are recorded. One stimulus
at 543 nm excites the light tracking receptor and the other at 660 nm excites chlorophyll. The response
of each cilium during the power stroke and recovery stage of the stroke is determined. The stroke
frequency, power stroke amplitude, and phase relationship between the two cilia are presently assayed.
The three responses of the cilia to the stimuli are correlated with the input stimuli to determine the
linear and nonlinear dynamics of the phototransduction network of the cell. The well-regulated
experimental conditions provide real laboratory data with strict control of the environment suitable for
extensive model analysis. A parallel cascade method of system identification is used, approximating the
system response by a linear dynamic element followed by a static nonlinear element. The output of this
linear-nonlinear approximation is tested to see if the mean square error is reduced. If so, this
approximation is added to other parallel linear-nonlinear cascades and the residual of this
approximation is used as the successive input. Cascades are added until the mean square error of the
model is reduced to an acceptable level. This analysis yields first and higher order x-y and x-x
kernels. The stroke frequency, power stroke amplitude, and relative phase between the cilia all exhibit
highly nonlinear properties with different delays and characteristics. Each cilium also possesses unique
characteristic responses. In addition to light stimuli, chemicals may be used to stimulate
Chlamydomonas. Mutations at known points of the signaling pathway and mutants with defects in the
ciliary mechanisms and photoreceptor can be assayed to elucidate the mechanisms of the cells? signaling
network. Questions of the precise nonlinear control of the cilia as well as the fractal nature of the
control and signal processing are being addressed. 

 Partha P. Kanjilal - US
Army Research Institute of Environmental Medicine, Heller Institute of Medical Research

Characterization of Heat Intolerance Response Through Orthogonal Transformation Based Analysis of Heart
Beat Interval Series. Partha P. Kanjilal, Richard R. Gonzalez, And Daniel S. Moran

 This study examines the heart beat interval (R-R interval) series obtained from a database of military
subjects who had suffered heat stroke. The data are collected following a heat tolerance test (HTT) in
which treadmill exercise (1.34m.s-1,2%grade for 2h) is performed in a climatic chamber with ambient
conditions of 40fC/40%RH. The HTT is conventionally performed whenever a person is presumed to be heat
intolerant (HI). In HI individuals, the presence of inherent non-stationarity in the cardiac R-R
intervals series renders conventional spectral approaches unusable. Since wavelet transform is not
influenced by the non-stationarity, it is used to preprocess the data. We arranged the database from a
cohort of individuals who were HI and heat tolerant based on the HTT. The data were arranged into a
series of matrices up to row lengths n=30. Each data matrix was singular value decomposed. The set of
singular values (si, (1£i£ n)) obtained for each configuration were then mapped to a set of 30 singular
values (SV) preserving the overall energy, and a mean distribution of the set of singular values is
formed. Subsequently, a normalized and weighted (by i2) SV profile was generated. The {i2si}
distribution was scaled by (i2si)max and is plotted against i. It is observed that for healthy
heat-tolerance individuals undergoing HTT, the normalized and weighted singular value distribution
profiles tend to collapse together. In the present context, the distributions for the heat-tolerant
group tend to be quite close to each other, whereas the distributions for the heat-intolerant group were
highly dispersed. It is suggested that the dispersion of the profiles of the weighted singular value
distribution may be a useful analytical prognosticator of possible heat-intolerance in otherwise healthy
humans. Further studies are underway to validate this finding. 

 Igor E.
Kanounikov - Saint-Petersburg State University

Correlation Dimension Analysis of the Human EEG in Visual-Attention Tasks

 Thirteen healthy subjects participated in the study. The visual-attention tasks consisted of a set of
circles and squares appearing occasionally to the left or to the right of fixation point in the center
of the monitor screen. The subjects received three instructions in different series: 1) press the button
in response to any stimulus appearing to the left (to the right) of fixation point; situation Where
press the button in response to circle (square) independent of presentation side press the button in
response to the circle (square) presenting to the left (to the right) of the fixation point (situation
“What and Where EEG was recorded monopolarly according to international system 10-20 in 6
connections: T3, T4, T5, T6, P3, P4 Correlation dimension D2 of EEG was estimated according to
background EEG epochs prior to stimuli presentation. All data were averaged for the subject group.
Grassberger-Procaccia approach was used for correlation dimension is estimation. During data analysis we
proceeded from the fact that the EEG correlation dimension is the measure of process complexity and
reflects the number of independent generators participating in its genesis. The dimension analysis
increase in the interval prior to stimulus was regarded as a preparation set manifestation of the given
area to information processing. Two reliable differences were revealed as a result of comparison of
correlation dimension of EEG values obtained in three situations: 1) in situation What D2 values in the
left temporal site were significantly greater than in situation Where ( 5.00 vs 4.61; P< 0.05) , and in
the left parietal site contrary interrelations existed.(4.32 vs 4.72, P< 0.05) The results obtained
confirm that in visual system two relatively independent systems of information processing subsist: the
system Whatis located in temporal areas and responsible for the object recognition whereas the system
Where is located in parietal areas and defines an object localization. 

 Eve
Mitleton-Kelly - London School of Economics

Organizational Complexity

 Work-in-progress will be presented on a research project looking at the conditions that facilitate the
emergence of new organisational forms (or ways of organising) after a merger, restructuring or the
spinning off of a new business. This is a 3-year collaborative action research project with 5 business
partners: British Telecom's Brightstar (an incubator of new businesses), Norwich Union Life and
Rolls-Royce Marine (both recent mergers), Shell Internet Works (spinning off new businesses leveraging
the Shell brand and global presence)and BTA consulting who will help with the dissemination. The project
will use the principles and logic of complexity to study: - the integration of national, business,
cultural and technical systems in the emergent organisational forms; - the role of ICTs in facilitating
connectivity and the exchange of knowledge; - the tension between globalisation and local cultures and
requirements. We have a research team of 12 including an artist, a modelling expert, two psychologists
and a business liaison manager, as well as an international team of business and academic advisors. The
paper will outline the complexity logic being used in the methodology and present some initial findings,
including a short study on virtual teams when one of the business partners set up a new business in
another continent. The issues raised include 'management at a distance' when the head office is still in
London; knowledge generation and sharing; the use of ICTs to enable communication and knowledge
processes. Learning from the four pilot studies, both by the parent organisation and by the other
business partners is a key element of success and the project is using various methods to facilitate
interchange and learning. The Complexity Research Programme at the London School of Economics was
awarded the largest research grant given to the School for social science research, by that particular
Research Council and the examining Panel gave the project the highest priority. 


Mark Klein - MIT

A Complex Systems Perspective on Collaborative Design

Collaborative design is challenging because strong interdependencies between design issues make it
difficult to converge on a singledesign that satisfies these dependencies and is acceptable to all
participants. Complex systems research has much to offer to the understanding ofthis process. This paper
describes some insights derived from this novel perspective. 

 Harold E. Klein
- Temple University

Designing Organizations to be Responsive to the Complex Changing Environment

 Organizations cannot seem to change fast enough in response to the deconstruction of conventional
industries within which they compete - let alone, to the potential structural changes that can only be
conjectured. In response, industries are consolidating. Through acquisition and merger, corporate
organizations are reaching such size and internal complexity as never before experienced. And this trend
is sure to continue! The problem isn't confined to corporate organization. While the post war geopolitic
has disintegrated during the last decade, national governmental institutions (here and abroad) designed
to interface with the rest of the world have remained essentially unchanged. The problem is made
frighteningly clear in the USG's organizational response to 9-11: the creation of the Office of Homeland
Security (OHS), an organization that is supposed to coordinate (?) the efforts (or Programs or...?) of
some 126 governmental entities. The organizational chart distributed by the OHS soon after its creation
showing its sphere of responsibility crystallized the difficulty - an obtuse, confusing and
unintelligible layout of existing interrelationships among the 126 agencies that are relevant in some
way to the national anti-terrorism effort. New challenges are being met with administrative
infrastructures that evolved to cope with environments that no longer exist. The issue is how to evoke
representations of prospective environments that can assist decision makers in reconfiguring their
internal organizational arrangements so as to be responsive to changing conditions - to provide the
answers to such questions as: Which strategic decisions/activities/organization units need to take
action? What is the sequence in which these decisions//.../ to be addressed or act? Which organization
units/decisions/tasks need to be coordinated in response to a prospective environment? Given alternate
views/forecasts/scenarios, what changes are necessary from the current infrastructure? How can
organization actions alter the prospective environment? Where are the most appropriate intervention
points? And which organization units/activities/ tasks are involved? In a previous NECSI conference, I
presented both the conceptual foundations of the SPIRE methodology for representing the organization's
relevant environment in a unique causal mapping format that can be helpful to strategic decision-makers.
I also summarized the results of an actual application of the approach in a large corporation, focusing
on one of several revelatory causal maps generated from inputs from the corporation's own environmental
scanning task. Here, I will focus on the organizational design implications, i.e., the answers to the
questions posed above. I will contrast these with the original organization structure and show what
changes are indicated. The SPIRE approach, data availability permitting, will be applied to published
OHS organization data in order to show how a complex systems methodology could assist in implementation
of the OHS mandate, such as it is understood. The SPIRE approach (Systematic Procedure for Identifying
Relevant Environments) is based on a heuristic program that creates causal map representations of the
organization environment closely analogous to neural networks, both in appearance and behavior. To my
knowledge, it is the only operational tool currently available for strategic decision-making. 

 Mark Kon - Boston University

 Learning theory now underlies much of what we as a society are trying to accomplish technologically.
This field subsumes a number of areas of inference, including neural network theory, nonparametric
statistics, statistical learning theory, and classic machine learning. Its importance stems from the
fact that most human endeavors with well-defined objectives can be encoded in input-output or
stimulus-response terminology. This in turn is best representable mathematically as a function f from a
space of possible stimuli (inputs) to the space of possible responses (outputs). Such a function may for
example: � map a picture to a correct parsing of its contents � map visual inputs of biological system
to correct behavioral responses � map a pattern of object characteristics to a correct indentification
of the object � map a sequence of amino acids to a protein's structure or its chemical behavior � map
values of a function to the estimate value of its integral � map values of a function to a an estimate
of the entire function The basic learning problem is that of learning (identifying) f. To do this, an
individual or machine must see examples of it. In natural and artificial learning, this is accomplished
through observation of correct outputs f(x_i) to given inputs x_i . This paradigm began in classical
statistics (e.g., learning a linear regression function from data points). It continued into the more
complex domain of nonparameteric statistics (e.g., learning a nonlinear function from data points).
Nonparametrics then diversified into feedforward neural network models, which have been included in
parts of continuous (or statistical) learning theory. Meanwhile, the discrete version of continuous
learning theory has long been studied in (machine) learning theory, a field which had primarily resided
in computer science. Data mining encompasses all these areas with a paradigm of ever-increasing
complexity of databases and models. Fortunately, a re-integration of these sub-areas is now occuring.
This special session is devoted to various areas of current research in learning theory, with the
sub-text of emphasizing the ever-increasing complexity of both the tasks handled by these methodologies,
and of the methodologies themselves. The learning problem being dealt with by this spiraling complex of
approaches now appears in many different formulations, and yet has not lost its simple basic nature. 

 Elena Kondratskaya - Bogomoletz Institute of Physiology

Effects of Ginkgo Biloba Extract Constituents on Main Membrane Conductance Mechanisms in Neurons of Rat.
Elena L. Kondratskayaa, Shyam S. Chatterjeeb, Oleg A.

 Active compounds of Ginkgo biloba extract (ginkgolides A, B, C, J, bilobalide and its synthesized
analogue NV-31) were tested on main membrane conductance mechanisms in neurons from hippocampus,
cerebellum, dorsal ganglion root, trigemenal ganglion root using a conventional patch-clamp technique in
the whole-cell configuration and concentration-clamp recording technique. It was found that all examined
ginkgolides reduce glycine-activated currents in concentration- and use-dependence manner with IC50
values 1.97; 0.273, 0.267 and 2.0M for ginkgolides A, B, C and J respectively. All ginkgolides have not
affected else tested ligand-gated receptors (NMDA-, GABA-activated). The dose-response curves on
glycine-induced currents (elicited by 200 M) for pairs of ginkgolides B-C and A-J are not enough
discernible. The Hill coefficient in all causes was close to unity, what indicates single site of drug
binding to glycine-activated receptor. These data are not comparable with facts concerning their action
on PAF receptors, where similar activity is typical for another pairs A-B and C-J. Hence, the mechanisms
of ginkgolides action on glycine- and PAF-operated receptors are completely differing. We have screened
the action of bilobalide and NV-31 31 (4-hydroxy-4-tert-butyl-2,3,5,6-tetrahydrothiopyran-1-oxide),
bilobalide analogue, on the rank of voltage-dependent and ligand-gated channels in isolated neurons from
different tissues. Bilobalide has revealed the inhibition activity on NMDA-activated currents while have
not any spastically recognized effect on another receptor/channels. This activity was not dependent on
co-agonist concentration (1M-1mM), which indicative another mechanism of action than via glycine
strychnine-insensitive site of NMDA receptor. NV-31 (10;M) displays the potentiation of inhibitory
ligand-gated channels, glycine-activated receptors, as well as GABA-activated ones (I/I0 values were
119.8 % and 139 % respectively). Bilobalide analogue was more active relatively inhibitory transmission
in hippocampal neurons, whereas natural bilobalide affects excitatory NMDA-ergic transmission. Earlier
described facts compared with presented data could explain the mechanisms of summary Ginkgo biloba
extarct action on CNS functions due to their active compounds interests: I joined the research group of
the Departmentof Cellular Membranology when I was interested in investigation of NMDA receptors on
mammalian neurons. Our experimental work is primarily based on using patch- clamp and concentration-
clamp techniques in whole cell configuration.My graduation thesis was concerned with the mechanisms of
modulatory action of hyperforine on NMDA receptors. It was the part of general work of our research
team, which was engaged in examination of the effects of different constituents of Hypericum perforatum
extract on the main receptor/channel systems on excitatory membrane of neurons. Later research of mine
consisted in determination the rank of the activity of amino-adamantane-derivatives - fast blockers of
NMDA channels. I have recently studied proton-activated receptors on dorsal root ganglia neurons and
modulation of them by neuropeptides. The principal idea of my project for obtaining the postgradute
degree is examination of series new glycine-gated channel blockers which were reported to be present in
Ginkgo biloba extract (ginkgolides) and first known as PAF receptor antagonists. The activity of
cyanotriphenylborate and picrotoxinin, the best studied blockers of the Cl pores, crucially dependents
on the subunit composition of receptors. CTB blocks 1 and 1/8;, but not 2 GlyRs PTX blocks homomeric
receptors 1 GlyRs, but weakly antagonizes heteromeric receptors 1/GlyRs. The developmental heterogeneity
of GlyR subunits is confirmed by molecular cloning studies. Our preliminary examination have revealed
that blocking action of Ginkgolide B does not vary with the alteration of the subunit composition of
this receptor during development. Moreover, the analysis of glycine receptors properties by using
electrophysiological teqniques will clarify the role of this type of ligand-gated receptors in
hippocampus, where the main inhibitory transmitter is suppose to be GABA. Consequently, the field of my
research is investigation of excitatory and inhibitory amino acids receptors, which are tightly
associated with ionic channels (N-methyl-D-aspartate, GABA and glycine) in the animal CNS. It is known,
that modified or impaired functions of these channels can correspond to different pathophysiology
condition. Therefore, screening for selective modulators of these channels is essential for designing
new clinical effective pharmacological substances. Therefore, this course of lectures would help me to
obtain more strong background in the field of intercellular signaling and depict more clear the possible
adaptation of our discoveries for some pathological states of glycine-operated receptor. Relations and
conference between scientiests in the same object are the first step to our co-education and combined
projects, writing articles and reviews. It is worth to say, that using combined electrophysiological,
immuhistochemical and biochemical methods will do the understanding molecular structure of tested
receptors more precise. 

 Konstantin L Kouptsov - New York University

Using a Complex Systems approach to undo Brainwashing and Mind Control

 An L-system is the way to describe or to generate the complex object by a set of production rules. In
this case the algorithmic complexity is eazily calculated. In the case when the exact rules are not
known an algorithm to search for production rules is proposed. If the object admits only an approximate
L-description, the algorithm can be modified to use heuristic match. Konstantin Kovalchuk - National
Metallurgikal Academy of Ukraine Description of a Subjective Position of a Decision-Maker in Human
Organization It counts traditionally, that the decision maker's (DM's) subjectivism decreases the
quality of decisions and that it is important to get rid of it. But in our point of view it is in
conformity with the practice of economic management, too; the subjectivism of a DM not only does not
decrease the quality of economic decisions, but also increases their flexibility, makes them more stable
against the various kinds of influences, provides the practical use of decisions and responsibility for
executing them due to the opportunity to express the DM's position or interest The decision-making
problem consists of approximating the binary relation over the set of the feasible alternatives
(decisions), using the information about DM's preferences. Let be a fuzzy binary reflexive relation For
the formal description of a subjective position of a DM let us introduce the monotonously increasing
reflection function as estimation of each alternative The DM stands by the position of neutrality, if
his reflection function is the identity- the position of pessimism, if the position of optimism, if
Except three basic positions (optimism-neutrality-pessimism) it is important to single out two combined
positions, too, the positions of centrism and extremism. The DM stands by the position of centrism if
his reflection function fulfills the condition: And - the position of extremism if his reflection
function fulfills the condition: Classification of the DM's positions allows to understand the
dialectics between the objective estimate of an alternative and the subjective position of a DM: "A
subject appreciates more precisely the alternatives, which objective estimates are opposed (contrary)
against his own position." So the pessimist estimates more precisely the best alternatives, the optimist
the worst ones, the centrist the extreme (best and worst) ones, and the extremist the average ones. Thus
it is so important to count the subjective position of a DM depending upon the specific of the problem
economic situation. The reflection function of the concrete DM is obtained by the expert way. This
process can be simplified considerably if the DM's subjective position is known a priori. To select the
kind of the function we can use the power function, which is concave when, but is convex on the interval
for. Realizing the group expert analysis, the additional requirement is placed upon the team of experts
the aggregate neutrality, which we can formally describe, by condition: Where the coefficient of
competence of -the exper the reflection function of -th expert's position. Conclusion. The model of
abstracting and retrieving the DM's subjective position, which increases the realizing or the economic
decisions has been developed. For that the subjective 'positions of a DM, based upon the scales
"pessimist-neutral-optimist" and "extremist-neutral-centrist" are formally defined arid described. It is
shown formally, that the opponent estimates more exactly the alternatives, which are correspondent to
the opposite position, specifically, the pessimist the best alternatives, the optimist the worst ones,
the extremist the average quality of alternatives, the centrist the extreme ones. Practically it
motivates the objective requirement to form the team of experts among the people with various subjective
positions. 

 Masaharu Kuroda - Applied Complexity Engineering Group, AIST

Local Complexity and Global Nonlinear Modes in Large Arrays of Fluid Elastic Oscillators. Masaharu
Kuroda, Francis C. Moon

 Process from local complexity to global spatio-temporal dynamics, especially the generation mechanism
of a traveling 2D-wave is investigated in a group of nonlinear oscillators such as arrays of
cantilevered elastic rods in a wind tunnel. Generally speaking, the soliton theory tells us that only a
soliton-like wave can exist in systems with dissipation. A solitary wave can appear even in real
engineering systems with energy dissipation only if a nonlinear effect and an energy-input compensating
the energy-loss by damping exist. This prediction is now being proved experimentally.From 90 to 1000
steel and polycarbonate rods with gap ratios ranging from 1.0 to 2.5 are used. As the Reynolds number
(based on rod diameter) increases from 200 to 900, a pattern with characteristics of spatio-temporal
chaos emerges in global behavior of the elastic rod array. There are local and global patterns. Local
patterns are composed of transient rest, linear motion, and elliptical motion. In the 90-rod
experiments, a cluster-pattern entropy measure is introduced based on these three patterns as a
quantitative measure of local complexity.Below a threshold wind velocity, no significant dynamics
appear. Video images reveal that, at first, each rod moves individually, then clusters consisting of
several rods emerge and, finally, global wave-like motion takes place at higher flow velocities. Spatial
patterns in the rod-density distribution appear as more rods suffer impacts with nearest neighbors.
Furthermore, these collective nonlinear motions of rods are observed and categorized into several global
modes. Using accelerometer data, the rod impact rate versus flow velocity shows a power-law scaling
relation. This phenomenon may have application to plant-wind dynamics and damage as well as
fluid-structure heat exchange systems. This experiment may also be a two dimensional analog of impact
dynamics of granular materials in a flow. 

 Chang-Yong Lee - Kongju National
University

A Stochastic Dynamics for the Popularity of Websites

 As the Internet plays an important role in our present society, research on the Internet becomes more
and more active. In particular, study of the characteristics of websites and their dynamical phenomena
has become recognized as a new field of research. Aside from the technical understandings of the
Internet and the web, this new field can be regarded as an artificial ecological system of which many
interacting agents, or websites are composed. As is true for most complex systems, size and dynamical
variations make it impractical to develop characteristics of the web deterministically. Despite the fact
that the web is a very complex system, seemingly an unstructured collection of electronic information,
it is found that there exists a simple and comprehensible law: the power law distribution. It is known
that the number of visitors to websites also exhibits a power law distribution. This finding suggests
that most of data traffic in the web is diverted to a few popular websites. This power law distribution
of the popularity for websites is one of the characteristics of the Internet web market. In this paper,
we have studied a dynamic model to explain the observed characteristics of websites in the WWW. The
dynamic model consists of the self growth term for each website and the external force term acting on
the website. With numerical simulations of the model, we can explain most of the important
characteristics of websites. These characteristics include a power law distribution of the number of
visitors to websites, fluctuation in the fractional growth of individual websites, and the relationship
between the age and the popularity of the websites. We also investigated a few variants of the model and
showed that the ingredients included in the model adequately explain the behavior of the websites. 

 Jeho Lee - KAIST

Reconsideration of the Winner-Take-All Hypothesis

Recently, the winner-take-all hypothesis has been popular not only in academia but also in industries.
This hypothesis has been drawn from prior research on network externalities, which showed that
competition between incompatible technologies would make the market tip toward a single dominant
technology or firm. Prior work stressed the importance of a large installed base by implicitly assuming
that consumer benefits arise globally from all the adopters in a network. Our study is based on a
different assumption: Each consumer's benefit from adopting a technology is primarily affected by her
close acquaintances. We found that market dynamics do not always lead to winner-take-all. The results
depend on the assumptions on kinds of network topologies. Our results can potentially address the
question of why incompatibilities sometimes persist. 

 Denys Lèpinard -
Researcher

Universal Classification of Living and Non-Living Beings

 Distinguishing two categories in the structure of natural systems, the author presents a new structural
classification: some natural systems (called individuals) are constituted like animals by an assemblage
of a few different organs, others (called assemblies), like one of those organs, are built from a great
number of similar individuals. This classification applies to biological and no biological beings as
well as to heavenly bodies. Therefore, knowing that the definition of individuals and assemblies implies
an alternation, the going back phase naturally sets up a level. On the table 1, we may see 6 of them,
numbered from 0 up. They are actually organization levels due to the fact that they are built on
structure. Moreover, they are also complexity levels : they are set up on the number of elementary
components which multiplies at each level, and also complexity increases with the number of elementary
components to organize and make work together. Then the author shows it is possible to do the same work
for heavenly bodies. Now if we place on a graphic the masses of certain noteworthy individuals on regard
with their level we obtain a striking regular sinusoidal curve (graph 1). By its regularity, this curve
proves that these spots are not at random, instead such disposition point out that natural systems
distribute their masses according to their organization level. This seems to indicate that individuals,
as we defined them, fix themselves on places where their masses -that is the number of protons they
gather- is optimum. There, they function better, their relations between other individuals of lower and
upper level work better. Then the author displays that the mass P of noteworthy individuals increases
from one level L to another according to the relation: log P = 41(1-cosL/10). All that makes suspect any
still unknown, common to the living and no living matter, relation, such as some internal dynamic of the
universe. A full text is available on the site : http://www.ontostat.com 


Natasha Lepore - Harvard University

Unified Framework for Finding the Eigenstates of Helmholtz Equation Using Boudary Methods

The powerful plane wave decomposition method (PWDM) for finding the eigenstates of the Helmholtz
equation can be regarded as a variant of the mathematically well-established boundary integral method
(BIM). The capabilities of the BIM and the PWDM are discussed using a unifiedframework. This opens the
way to further improvements. 

 Ellen Levy - School of Visual Arts, NYC

Initial Conditions to Final Results in the Complex Systemsof Art and Science

 Systematic approaches to making art share certain methodologies with conducting scientific experiments,
such as establishing a set of parameters and constraints to set a process in motion. Approaches may also
include replication, creative tinkering Despite these similarities, the evaluation criteria of these two
activities encompass different aims and modes of address; in fact, different languages. In contrast to
science, where reproducibility of results is valued, qualia are often evoked in the arts. But this begs
the difficulty of assigning consistent, shared values used in assessing art since for art, the intention
as well as the end result is what counts especially where replication occurs (e.g. appropriative visual
strategies). In certain ways, complexity approaches help erode this presumed subjective vs. objective
distinction for the arts and sciences. Cultural models of innovation and learning bear particularly
meaningful analogy to complex adaptive systems in biology (e.g., processes of biological mutation and
adaptation). Artists as well as scientists can portray how novel innovative or adaptive utilizations may
lead to a breakup of constraints, yielding unpredictable results. The morphologist D'Arcy Thompson is
relevant to this discussion as are others, including scientists and artists, who have modeled dynamic
change over time. Artistic approaches both with and without extensive technology can focus insightfully
on evolutionary processes. Along with other artists who have work experience in science, I have found
ways to adapt generative and visualization techniques to topics of biological and cultural evolution.
With our greatly expanded technological means to simulate outcomes, the real has at times become
co-extensive with the artificial. Complexity theory plays a role in mediating these changing conceptions
of nature and culture. 

 Jacques Lewalle - Syracuse University

In What Sense is Fluid Turbulence a Complex Physical System?

Fluid turbulence is a familiar fluid phenomenon, from the billowing wind to the mixing of creamer in
coffee. It has long fascinated artists (da Vinci's sketches, van Gogh's Starry Night, Hokusai's crashing
waves), and challenged physicists, mathematicians and engineers. Turbulence is still, with various
attributions, the biggest unsolved problem of classical physics. It is a perennial attractor of the
latest theories and concepts, such as statistical mechanics, stochastic modeling, catastrophe theory,
renormalization,chaos, and self-organized criticality, to name only a few. The difficulty resides in
finding the proper interface between these ideas, the rich physics of the phenomenon and the
Navier-Stokes equations (NSE), which are the fluid-mechanical version of F=ma. In their standard form,
NSE are a nonlinear system of partial-differential and nonlocal equations; known since 1823, they have
yielded only a few exact solutions, none relevant to the understanding of turbulence. Noteworthy
alternative formulations include the Fourier version of NSE and the use of vorticity instead of Newton's
momentum. In spite of partial successes, none of these approaches has captured the emergence of eddies
in three-dimensionalfields (although they appear in numerical solutions), or the statistical scaling
laws that connect eddies of different sizes. In this paper, a promising new approach is presented,
making use of wavelet transforms. The spatial-spectral wavelet representation of the velocity field
corresponds to the addition of one independentvariable, which modifies the mathematical structure of
NSE. Relevantto the emergence of structures, nonlinear algebraic interaction rules are identified; their
symmetries and spectral content will be presented and differences between two- and three-dimensional
turbulence will be discussed. 

 Xuenan Li - University of Massachusetts

The Complexity of the Growing Network 

 Growing network models of the web and
other complex system produce pparently complex networks from simple growth rules in which nodes are
added one at a time to the network. The probability that a new node links to an existing node of the
network scales as a power \gamma of the degree of the existing node. When \gamma=1, the growing network
self-organizes to a power law degree distribution in qualitative agreement with complex networks found
in the real world. We show, however, that these growing network models, though they seem to require that
nodes are added one at a time, lack the true history dependence. We exhibit an efficient parallel
algorithm for generating these networks for the case \gamma=1. The running time of the algorithm is
polylogarithmic in the size of the network showing that the model is not complex in the sense of
parallel computational complexity. The cases \gamma \neq 1 are also studied using a Monte Carlo
algorithms.

Xiang San Liang - Harvard University

A Multiscale Interactive Dynamical Analysis for Oceanic Flows Intermittent in Space and Time. Xiang San
Liang and Allan R. Robinson

 A new methodology, Multiscale Energy and Vorticity Analysis (MS-EVA), has been developed to investigate
sub-mesoscale, mesoscale, and large-scale dynamical interactions in oceanic free jets. MS-EVA is based
on a new device called a multiscale window transform (MWT). This is a local, orthonormal, and
self-similar functional analysis tool which is windowed on scales, with location resolution maximized in
the phase space. With this transform, multiscale features are represented in distinct scale windows. The
energetics and enstrophy for these windows are then defined and their governing equations derived. For
each window, the resulting equations show a balance of terms representing processes which can be
categorized into three classes: transport, transfer, and dissipation. Among the transfer processes, of
particular interest are those perfect transfer processes, which, for every location in the physical
space, act to re-distribute the energy over the phase space, but with the energy sum over the scales
preserved. The perfect transfer processes can be further decomposed through interaction analysis to
describe the energy source information. When properly combined, these perfect transfer interaction
analyses are shown to correspond to important processes in geophysical fluid dynamics. Barotropic and
baroclinic instabilities are two such examples. Two classical models, each representing one of these
instabilities, are chosen to validate the MS-EVA. As a real ocean application, the MS-EVA is used to
diagnose the dynamics of the Iceland-Faeroe Front (IFF). An MS-EVA-ready dataset is first generated,
from a hindcast with the Harvard Ocean Prediction System (HOPS), using the hydrographic data obtained
during a 1993 survey in that region. The observed mesoscale meander provides an ideal testing ground of
the MS-EVA capability. The calculated energetics, when locally averaged, reveal that the formation of
the meander is a result of both a baroclinic instability and a barotropic instability, with the former
dominant in the western region at mid-depths, while the latter is more active in surface layers. 

 Seth Lloyd - MIT

Bits and Bucks: Modeling Complex Systems by Information Flow

 This talk presents a general method for modeling and characterizing complex systems in terms of flows
of information together with flows of conserved or quasi-conserved quantities such as energy or money.
Using mathematical techniques borrowed from statistical mechanics and from physics of computation, a
framework is constructed that allows general systems to be modeled in terms of how information, energy,
money, etc. flow between subsystems. Physical, chemical, biological, engineering, and commercial systems
can all be analyzed within this framework. Take, for example, trading over the internet. Each flow of
information (measured in bits per second) is associated with a flow of energy (measured in watts). The
energy per bit -- effectively, a form of temperature -- is a crucial quantity in characterizing the
communications performance of the network in the presence of noise and loss. But each bit can also be
associated with a monetary value (bucks), as when the title to some commodity is transferred
electronically to a buyer and an electronic draft to pay for the commodity is transferred to the seller.
The bucks per bit -- again, a form of temperature -- is a crucial quantity in deciding whether to buy or
sell. Clearly, some bits are worth more than others! This paper shows that in complex systems that can
be accurately described by such a modeling framework, different structures for interconnects and
protocols for exchange can lead to qualitative and quantitative differences in behavior. In some cases,
such as thermodynamic systems, stable behavioral equilibria exist and exhibit gaussian fluctuations. In
other cases, such as phase transitions and systems of economic exchange, quasi-stable or unstable
equilibria exist and exhibit power-law fluctuations. Finally, some types of flows yield no equilibrium
at all. The framework makes quantitative predictions for the efficacy, flexibility, stability, and
robustness of complex systems characterized by flows of information together with energy, money, etc. 

 Corey Lofdahl - SAIC

On Trade and the Environment as a Complex System

 Issues regarding trade and the environment have gained increased policy salience as highlighted by the
1999 World Trade Organization (WTO) ministerial in Seattle. Economists maintain that trade helps the
environment citing numerous empirical studies that correlate international trade with increased national
wealth and national wealth with cleaner natural environments. Environmentalists, in contrast, maintain
that the opposite as environmental degradation is historically coincident with industrialization and
trade. Lofdahl (2002) argues that trade hurts rather than helps the environment using a range of
computer-based techniques including data visualization, statistics, and system dynamics. This study
highlights the complex system concepts that underlie this work. 

 Robert K.
Logan - U of Toronto

What the Evolution of Notated Language Teaches Us about the Origin of Speech

The origin and evolution of human language which has puzzled scholars for hundreds of years, has been
difficult to address is because the events took place hundreds of thousands of years ago and hence no
documentary data exists to shed light on what happened. Nevertheless many attempts to probe the origins
of speech have been made from a variety of different disciplines and data sets. The aim of this study is
to juxtapose, compare and synthesize this large body of work with a model I developed to describe the
evolution of notated language in The Sixth Language (Logan, 2000) where it was shown that speech,
writing, mathematics, science, computing and the Internet form an evolutionary chain of languages. I
believe that these two bodies of work can inform each other. The thesis that will be developed in this
paper is that historic data relating to the evolution of language after the advent of speech and
beginning with the emergence of writing can shed light on the origin and evolution of human language and
resolve some of the controversies and differences of opinion on a number of issues. At the same time the
origin of speech body of work can enrich our understanding of the evolution of notated language. 

 Irakli Loladze - Princeton University

Biological Systems From the Perspective of Chemical Elements: The Simplicity and Rigor of Stoichiometric
Approach

 Biological systems exhibit enormous complexity, yet the number of chemical elements in all life is only
a few dozens. Life cannot create or destroy chemical elements, nor can it convert one element into
another. These fundamental constraints are true for all scales of life from cells to biosphere.
Stoichiometric theory takes full advantage of these scale invariant constraints and provides simplifying
framework for studying biological dynamics. I will give examples of how stoichiometric approach can
provide qualitatively new insights: from ecological theory of predator-prey interactions to links
between elevating CO2 and human nutrition. 

 Erzilia Lozneanu - University
Iasi, Romania

Cell-Like Space Charge Configurations Formed by Self-Organization in Laboratory. E. Lozneanu and M.
Sanduloviciu

 A scenario of self-organization explaining the emergence, under controllable laboratory conditions, of
a complex space charge configuration that represents, in our opinion, the simplest possible system able
to reveal behaviors usually attributed to living systems, is described. Thus, similar to biological
cells, the boundary of the complexity provides a selective enclosure of an environment that
qualitatively differs from the surrounding medium. The boundary appears as a spherical self-consistent
electrical double layer able to sustain and control operations as: capture and transformation of energy,
preferential and rhythmic exchange of matter across the system boundary and internal transformation of
matter in the ongoing synthesis of all its components. Similar to biological pattern formation [1] the
basic mechanism at the origin of the complex structure is a local self-enhancement process complemented
by long-range inhibition [2]. After its emergence, the complexity is able to replicate by division and
to emit and receipt information. Such complex space charge configurations appear in a gaseous medium
containing neutrals and free electrons when an electrical spark creates a well-located nonequilibrium
plasma [3]. Because of the high thermal diffusivity of electrons with respect to that of the positive, a
plasma enriched in positive ions appears. When the temperature of this well located plasma exceeds a
critical value, its potential becomes so high that the surrounding electrons are accelerated towards it,
reaching energies sufficient to produce excitation and ionization processes. Under such conditions a
scenario of self-organization is initiated, the final product of which is a complexity revealing all the
above-mentioned behaviors [4-7]. Since electrical sparks able to create such initial conditions are
present in the earl Earth atmosphere, the scenario of self-organization described in this paper offers a
new possibility to explain the origin of life, considering physical phenomena well identified in
laboratory experiments. A similar scenario of self-organization was proposed to explain the appearance,
under contemporary Earth conditions, the ball lightning [3]. This scenario comprises a series of
physical processes, initiated by a spark, the natural evolution of which is finished by the
self-assembling of a gaseous framework. Its genesis proves the ability of Nature to create complexities
by locally defying the laws of thermodynamics. REFERENCES: 1. A .J. Koch and H. Meinhardt, Rev. Mod.
Phys. 66 (1994) 1481.2. E. Lozneanu and M. Sanduloviciu, Int. Conf. Phen. Ionized Gases, 2001 Nagoya
Japan, Proceedings vol. 3, p. 165.3. M. Sanduloviciu and E. Lozneanu, J.Geophys. Res. 105 (2000) 4719.4.
M. Sanduloviciu, C. Borcia and G. Leu, Phys. Lett. A 208 (1995) 136.5. M. Sanduloviciu, J. Tech. Phys.
38 (1997) 265.6. M. Sanduloviciu. Rom. Rep. Phys. 49 (1997) 475.7. M. Sanduloviciu, E. Lozneanu and S.
Popescu. J. Plasma Fusion Res. 2 (1999) 486. 

 Ram Mahalingam - University of
Michigan

The Fate of the Girl Child: A Systems Approach to the Evolutionary and Cultural Psychology of Female
Infanticide. Ram Mahalingam & Kanchana Ramachandran

 Son preference and female neglect have been common among many Asian cultures, particularly India and
China. Several theoretical perspectives attempt to explain why such extreme forms of female neglect and
infanticide exist in these cultures. Behavioral ecologists have argued that peer competition and female
explain why such extreme forms of female neglect and infanticide exist in these cultures. Behavioral
ecologists have argued that peer competition and female strategies for maximizing reproductive success
account for differential investments in sons and daughters. Cultural and ethnographic explanations focus
on the religious and cultural reasons for son preference, such as sons will take care of them when they
get old. However, female infanticide is a complex phenomenon. Preliminary data seems to indicate that in
spite of development interventions for womens empowerment through education and increased opportunities
for employment, female infanticide continues to grow in specific societies. Mahalingam and Low suggest
that a confluence of structural, cultural and ecological factors shape the history, current and future
practice of female infanticide. Using the interdisciplinary perspectives suggested by Mahalingam and
Low, we propose a complex systems perspective which incorporates the interaction among individual and
family level factors (gender socialization), caste identity (honor culture),and structural factors
(social ability, and developmental programs). We have identified one such caste group- the Thevars
amongst whom female infanticide is prevalent. Thevar, a warrior caste believes that caste honor needs to
be protected by marrying their daughters into higher status males of the same caste. Failure to do so
will be hurt the honor of the self and the primary goal of the caste is to protect honor at all cost. We
argue that the social mobility of Thevar women and Dalits (former untouchables) result in confrontation
between the two caste groups. State sponsored educational empowerment programs targeted toward Dalits
and Thevar women result in the mobility of Thevar women and Dalit male and females. Thevar males feel
threatened by the social advancement of Dalit men who are in competition to become attractive choices
for marriage. As a result there are several attacks on Dalits by Thevars resulting in inter caste
conflict. We argue that empowerment and the disparities in social mobility between Thevar men, women and
Dalits contribute to the increasing cost of marrying off Thevar women. This results in a negative
attitude toward having daughters. Thevars also feel that they need sons; to stand up for their caste. We
make the case that this complex interaction among structural factors such as caste hierarchy and social
development is emergent from and determines family dynamics in this caste. Thevar women are socialized
towards increasing femininity to prove their loyalty to their caste and their peers. This explain one of
the paradoxes in demographic findings that in this caste group, the more educated women are more likely
to invest heavily on their sons not on their daughters. The compliance of Thevar women to be more
traditional even after access to higher education attests to these complex interactions among gender
dynamics, cultural expectations and shifts in caste hierarchies. Using systems and exploratory modeling
techniques we hope to delineate and study these dynamics (1) from a micro systems perspective at the
level of family and individual interaction, (2) understand the non linear impacts of globalization and
development on female infanticide at the macro societal level and (3) and explore optimal leverage
points that can reverse current trends of increasing female infanticide in this community. 

 Oded Maimon - Tel-Aviv University

Data Mining. Oded Maimon and Lior Rokach

 Data Mining is the science of searching through data for unknown and common patterns for the purpose of
constructing model that explains phenomena. Scalability to very large sets of varied types of input data
is an important feature that distinguishes data mining from traditional machine learning methods.
"Classical" machine learning algorithms have been applied with practical success in many relatively
simple and small-scale learning problems. However when trying to discover knowledge in real world and
complex databases, a different approach is required. In many disciplines, like operation research or
engineering design, as the problem become more complex, there is a general tendency to try to break into
smaller distinct but connected pieces. The concept of breaking a system into smaller pieces is generally
referred to as decomposition. The purpose of decomposition methodology is to break down a complex
problem into several smaller, less complex and more manageable sub-probl In this paper we present the
issue of using Decomposition Methodology for Data Mining generally and in learning task (also know as
classification problem) particularly. This work proposes a unifying framework for decomposition
concept's learning problems into smaller and more manageable learning tasks. We prove that the
decomposition methods developed here extends the envelope of problems that data mining can solve
efficiently. These methods also allows for better understanding of the results and efficient
implementation of the knowledge discovery conclusions. Problem decomposition's benefits include:
Increased performance (classification accuracy), conceptual simplification of the problem, making the
problem more feasible by reducing its dimensionality, achieving clearer results (more understandable),
reducing run time by solving smaller problems and by using parallel/distributed computation, and
allowing different solution techniques for individual sub problems. We prove the relevant features in
the our work. Problem decomposition's benefits include: Increased performance (classification accuracy),
conceptual simplification of the problem, making the problem more feasible by reducing its
dimensionality, achieving clearer results (more understandable), reducing run time by solving smaller
problems and by using parallel/distributed computation, and allowing different solution techniques for
individual sub problems. In this paper we describe an approach for supporting the systematic
decomposition of learning tasks. We first introduce our approach for arranging the different types of
decomposition in supervised learning. We formulate the problems mathematically, refer to related works
in the literature and suggest promising modus operandi. 

 M. Marko - Comenius
University, Slovakia

Transforming the World Wide Web into a Complexity-Based Semantic Network. M.Marko, A. Probst, A. Das.

 Tim Berners-Lee¹s term "Semantic Web" denotes the next evolutionary step of the Web, which establishes
a new machine-understandable layer of datafor automated agents, sophisticated search engines,
information integration and interoperability services. The main aim of this paper is to examine
available tools and current standards to outline a practical roadmap to transform this vision into
avisible benefit. We hope that our example will inspire similar activities within the complexity
community to achieve a synergistic effect of the³Web¹s full potential². We begin with a task to overcome
existing drawbacks of keyword-based search engines ignoring the variable semantic context of query
keywords. Anexample of such semantic confusion could arise from a search for the keyword ³ontology². The
problem is that the word has different meanings indifferent communities. The philosophy community views
it as (1) a 'science of being¹. The AI and Semantic Web communities view it as (2) a 'systemof shared
meanings'.A standard search result would likely yield a mixture of these two meanings. Hence, many of
the search results would not pertain in the givensituation. Ontology(2) does not only contain metadata
i.e. data about the data such as word definitions or instances, it also contains relationships amongthe
concepts defined. A large space for various inference algorithms is being created. In this manner, a
semantic search for "ontologyconstruction language" (or even a Natural language query ³What is the
language to construct ontologies?) could point to DAML+OIL related articles,although they would not
contain any of the search keywords. It would be because the application could access metadata in which
DAML+OIL isdescribed as a language used to construct ontologies. The application would be able to infer
this relationship thanks to the machine-readableformat of the embedded metadata. It is clear that the
development of consistent ontologies will play a crucial role in further Semantic Web application. We
focus our attention toNeural net models (see: Neural Net Model for Featured Word Extraction, A.Das,
M.Marko, A. Probst, M. A. Porter) and internet-based communitycollaboration as potential means to speed
up the creation of ontologies. Finally, we present a practical application of a semantic search withinan
existing electronic news provider. 

 Ian W. Marshall - BT exact

The Role of Complex Systems in the Management of Pervasive Computing. Ian W Marshall, Chris Roadknight
and Lionel Sacks

 Extrapolation of current trends for ownership of microprocessors [1] suggests that within 10 years it
is possible that many individuals will ownin excess of a thousand microcontrollers. If, as seems
increasingly likely, pervasive computing on this scale is realized, users will be facedwith a major
investment of time and money in configuration and maintenance activities. To minimize this impact it is
important to investigate waysof automating low level management processes and enabling many pervasive
computing devices to self-configure and operate almost autonomously [2].At the same time it is vital to
ensure that the management processes are able to adapt to new requirements and applications, since it is
likelythat developments will be extremely rapid and unpredictable [3]. Clearly any management system
that can meet these requirements is going to exhibit dissipative structures, and long range dependency
(leading toscale free properties and fractal dimension), much like many natural systems. We have
simulated the operation of a management system (inspired bya simple model of biofilm colonies) that
autonomously configures and maintains a network of up to 5000 heterogeneous devices. A key feature is
theability of nodes in the system to perform unsupervised in-situ learning by exchanging policies with
one another. This was achieved by combiningpolicy based management [4] with an evolutionary algorithm
derived from prokaryote biology [5]. The algorithm combines rapid learning (plasmidexchange), automated
configuration (limited motility of individuals) and evolutionary learning via a conventional mutation
based GA. New policiescan be evolved internally. In addition users are able to distribute new policies
to subsets of the devices that they control, using a weaklyconsistent gossip protocol derived from
fireflies [6]. Simulations have shown the system is able to create and maintain an organisation
thatperforms at least as well as conventional designs such as caching systems, whilst requiring no human
input or intervention. In particular thesystem copes extremely well with multifractal load derived from
real Internet traffic logs, once it has self-organized into a metastable criticalstate. To provide a
practical verification of the simulation results we have embodied the algorithm in an ad-hoc network
consisting of 20 nodes,where the nodes are intended to emulate distributed sensor controllers. Each node
consists of 3 sensors, 3 actuators, a 16 bit microprocessor andan infra-red transceiver. Initial results
are encouraging. Generalising and proving the capabilities of this system will require complex system
models. We are attempting to extend the reaction-transportmodels of Ortoleva et al [7], since they are a
good fit with the capabilities of our current system. We also hope that improved understanding
ofbiogeochemical processes will provide inspiration for extending the functionality of our system. This
work was partly funded by the Royal Society, London [1] L Gerstner, keynote speech at telecom 99,
http://www.ibm.com/news/1999/10/11.phtml[2]
http://www.research.ibm.com/autonomic/manifesto/autonomic_computing.pdf[3] R Saracco, J.R.Harrow and
R.Weihmayer ³The disappearance of telecommunications² IEEE 2000[4] M Sloman., "Policy Driven Management
for Distributed Systems", Plenum press Journal of Network and Systems Management, Plenum Press[5]
I.W.Marshall and C.M.Roadknight, "Emergent organisation in colonies of simple automata." 6th European
Conference on artificial life. 2001[6] I.Wokoma et al "Weakly consistent sychronisition" Policy 2002[7]
P.Ortoleva, "Geochemical self-organization" OUP 1994 

 Paula Matthusen - NYU

In Memory of an Anthill: Complexity Theory and Compositional Processes

 In memory of an anthill, is a composition for string quartet based on ideas from cellular automata
theory. The score is organized around the choices that individual musicians make to begin, interrupt,
and restart the composition at any time. Since each individual musician makes these choices in the
context of the choices of the surrounding musicians in the quartet, the composition self-organizes from
their interactions. The composition can move along several possible musical;routes with each route
leading to its own, unique ending. Musical routes are not predetermined, but occur naturally as players
react to each other's interpretations during the piece. If a performer does not like a particular way
the piece is heading, he or she can "restart" the performance, thereby directing it away from an
unwanted ending. The string quartet would likely consider some of these possible endings, such as those
that instruct the performers to break their instruments or throw their bows across the room, to be
undesirable. The presence of these endings enhances the dramatic nature of the piece and encourages the
quartet to self-organize and cooperate as they redirect the course of the piece away from possible
unwanted results. Within much western and non-western music, the direction and identity of pieces are
often determined by carefully arranged and established hierarchies between members of an ensemble and/or
with a particular dominance of a tonal area or musical line. in memory of an anthill bypasses these
hierarchies by relying on the tensions between improvisation and pre- composed music while the 77 rules
for performance ensure that each player has an equal voice in the ensemble. This balance results in an
unpredictable work that is never performed the same way twice and yet always remains sonically
recognizable and distinct. For the conference, I would present two separate recordings of the piece and
discuss its conceptual and structural aspects. The use of cellular automata theory as a foundation for
in memory of an anthill results in an exploration of existing musical hierarchies, their fragility, and
ability to change. 

 John Maweu - Syracuse Univ

Self Organized Criticality in State Transition Systems

 The presence of self-organized criticality in a specific kind of state-transition system is
demonstrated by the presence of a power law distribution obtained by empirical evolution to convergence.
The state-transition systems under investigation are monotonic and thus are guaranteed to reach a fixed
point. In order to produce the power law distribution as seen in the sandpile effect, a fortuitous
resemblance between raising programs in the lattice of monotonic state-transition systems and associated
marginal increases in fixed point size and dropping sand grains on a sand pile and avalanches observed
at the surface of the sandpile was seen by my advisor. Not every program investigated gave rise to an
observable power law distribution, so a search for a program on the edge of chaos was indicated. The
monotonic state-transition systems under consideration are positive logic programs over a finite
signature $\Sigma$. That is, functions $p: \Sigma \to 2^{2^\Sigma}$ which describe the evolution of
interpretations of $\Sigma$ by the cumulative one step iteration operator $\hat{T}_p(I) = I \cup \{ x
\in \Sigma \mid \exists y \in p(x) [ y \subseteq I ] \}$ are investigated. For any $p$, $\hat{T}_p$ has
a fixed point. A wider goal of these investigations is to determine just how these fixed points depend
on $p$. We know, for example, that positive logic programs exist as points in a lattice and that raising
a program, $p$, in this lattice also raises the fixed point of $\hat{T}_p$ in the lattice of subsets of
$\Sigma$. Raising $p$ may, however, leave the fixed point of $\hat{T}_p$ fixed, just as adding a grain
to a pile of sand may not cause an avalanche. This resemblance of our transition system to Bak-Sneppen
models of evolution was reinforced by considering the difference in the size of fixed points as the size
of an avalanche. The log of the frequency of avalanches was plotted against the log of the sizes of the
avalanches and it is clear that the plot is evidence of a power law distribution. 

 Gottfried Mayer - Penn State Univ.

Timescales Aspects Of Internet Access With Emphasis On The Situation In Developing Countries. Gottfried
J. Mayer, Atin Das, Carlos Gershenson, Mason Porter, Matus Marko, Andrej Probst

 Many examples of complex systems show some form of self-reference in the form of internal feedback,
auto-catalysis, etc. One might even assume that it is one of the defining properties of complex adaptive
systems because without any form of self-reference or self-reflection it is unlikely that learning and
adaptation can be achieved at any level of efficiency. In the context of human social interaction this
principle is also known in a more general sense as "practicing what one ispreaching.". On the other hand
there are a number of historical examples that seem to suggest that this principle does not enhance the
individual's or the organization's evolutionary fitness: Successful pop singers who protest against
social injustice become millionaires exploiting their workers, conferences researching the impact of the
Internet provide no Internet access to their participants and publish their results in printed
proceedings without Internet distribution. In this paper we try to give some semi-quantitative account
of conditions that lead to self-referential behavior within the complex systems community and provide an
estimate as to when an adoption of principles and methods of complex adaptive systems research can make
the transition to becoming evolutionary advantageous in the competition among complex systems agents.
Our conceptual and more philosophical discussions will be supplemented with some rudimentary empirical
data from our experience in publishing the Complexity Digest (www.comdig.org) since 1999. We also
propose and initiate a polling process to establish increasingly more accurate information about the
level at which complexity methods are applied within the complexity community. The empirical results
will be archived and updated on www.comdig.org/selfref. In the second part of our contribution we will
discuss universal features that are common to biological brains and social organizations in general, a
structure that has recently been named "Global Brain". The formation of cell-assemblies in the
biological brain is believed to be associated with cognitive events and feature binding in perception
and learning. In the context of a global brain we interpret gatherings of intelligent agents with the
objective to communicate intensively on a common topic -such as a scientific conference- as an event
analogous to the binding event of cell assemblies. We know from biological brains characteristic
time-scales of about 25ms are established. We know that in human communication there exist similar
"universal" time-scales that facilitate constructive interaction and the emergence of collective,
self-organized behavior. One of the shortest time-scales is that of synchronous interaction (e.g. 300ms
in conversation etc) but other time-scales determined by biological factors ("how long can a person sit
and listen to a speaker" ) can be of similar importance. Current mega-conferences with the order of 10^4
participants push the limits of the concept of "face-to-face" interactions among participants. We argue
that there exists a critical time-scale after which the efficiency of communicating the conference
content sharply decreases. From our experience with conference web-casts arranged by Complexity Digest
we estimate that such a critical timescale lies within one week after the end of the conference. 

 John Mayfield - Iowa State University

Evolution as Computation

 The existence of genetic and evolutionary computer algorithms and the obvious importance of information
to life processes when considered together suggest that the formalisms of computation can provide
significant insight into the evolutionary process. A useful computational theory of evolution must
include life and also non-living processes that change over time in a manner similar to that observed
for life. It is argued that when the evolution of life is partitioned into a DNA part and a soma or
phenotype part, that the DNA part may be properly treated as a computation. A specific class of
computations that exhibit biology-like evolution is identified to be the iterated probabilistic
computations with selection when selection is based on interactions among evolving entities. When
selection is not based on interaction with other evolving entities, the term optimization seems more
appropriate than does evolution. One implication of the view presented is that evolution is an emergent
property of certain information systems. Another consequence is that the mathematics of computation
apply directly to the evolution of DNA. The paper introduces an information like measure of complexity,
transcript depth, which incorporates the minimal computational effort invested in the organization of
discrete objects. Because organisms are based on information stored in their DNA, organism complexity is
constrained in a fundamental way by DNA transcript depth. 

 Dennis McBride -
George Mason University

Forecasting Simple (terrorist) Patterns Through the Modeling of Complex Interactions.

The prediction or forecasting of group behavior, in this case, specific terrorist cell activity, is
being pursued along several technological avenues by the Defense Advanced Projects Agency and its
several research organizations. This paper discusses some of the approaches and results at an
unclassified level, that are the product of previous DARPA investments in complexity research. 

 Robert Melamede - UCCS

Dissipative Structure Based Perspectives on the Origins of the Genetic Code

 Modern open system thermodynamics, as pioneered by Prigogine, provides a new framework for examining
living systems. This paper presents a variety of novel hypotheses that integrate opens system, far from
equilibrium physical principles with many of the layers of biological complexity. An abstract conceptual
development will provide a unifying perspective that starts with pre biotic evolution, the origins of
the genetic code, and the origins of life itself. A physical understanding of the nature of health and
disease will lead to thermodynamic interpretations of the basic living processes of cell division and
cell death. These underlying processes will be reiterated to provide new definitions of speciation and
individuality. Considerations of man's place in an evolving biosphere will also be examined. 

 Yasmin Merali - Warwick Business School

The Concept of Emergent Ecologies and Persistence in Cyberspace

Summary This paper focuses on the persistence of firms in the Internet-enabled, inter-connected world,
deploying the information lens to look at the shaping of competitive domains in the emergent
socio-economic context. We conceptualise the potential competitive space as a multidimensional
information space and explore the dynamics of its structuring as firms interact, creating dynamic
networks of networks with emergent competitive characteristics. We introduce a classification of types
of networks emerging in cyberspace, and suggest that in complex dynamic environments, networks of
networks give rise to persistent ecologies. The argument is illustrated by tracing, on a time-line, the
co-evolution of a long-lived technology company and its ecology. Using the concepts of emergence and
self-organisation derived from complexity theory, it is argued that it is at the level of the ecology
and through the emergent properties of the collective that cyberspace is structured, and that the
persistence of firms is intimately coupled with sustaining the ecology. Overview We begin by outlining
the context for competition in the information space. Much of the popular new economy literature focuses
on exploitation of the Internet for competing in, and reshaping, existing competitive spaces. The focus
of this paper is on the emergence of new competitive spaces and the dynamics of persistent firms shaping
and being shaped by the context, and affecting and being affected by discontinuities of the context. The
information lens is deployed to explore how organisational existence and interaction can shape and be
shaped by the information space that the Internet has opened up. It highlights the contemporaneous
emergence of new competitive spaces and the organisational development of ecological niches in the
information space. We set out defining characteristics of organisation in the information space,
establishing the co-dependence of firm-level organisation in the information space and the emergent
macro-level organisation of the information space. Arguing that the ubiquitous connectivity afforded by
the Internet effectively opens up an unbounded, multidimensional space for competition, we proceed to
examine emergence of organisational characteristics, capabilities and interactions through
colonisationof this space. The process of colonisation is seen as one entailing changes in the
characteristics of both, the colonising organisations and the territory being colonised. The example of
a long-lived technology company to show how collective interactions give rise to the emergence of a
viable ecology that sustains, and is sustained by, co-evolution its inhabitants. Drawing on analogies
from biological systems, the paper explores requisite organisational forms and characteristics for
competing in the Internet enabled economy. It concludes that the dynamic network offers the most
resilient organisational form for this context, and that evolution, adaptation and transformation are
all important for generating the requisite diversity of capabilities for defining and exploiting new
competitive spaces. The final discussion reflects on implications of this view for the theory the firm,
suggesting that the emergence and evolution of dynamic information networks in the information space
will challenge traditional views of the boundary of the firm and the articulation of inter-firm
transactional value propositions. 

 Czeslaw Mesjasz - Cracow University of
Economics

Changing Images of Organization and Development of Information Society

 In the early stages of development of management theory, a mechanistic metaphor had been used. Later it
was replaced with a metaphor of organism (biological system). A significant breakthrough in applications
of metaphors in management theory was caused by development of systems thinking and cybernetics (the
latter is viewed as a part of the former). Metaphors and analogies rooted in systems thinking, and
later, in complex systems studies, have become an important instrument of studies of organizations. The
following analogies and metaphors have been most useful in management theory and practice: machine,
biological system (living system), open system (related with the previous concept), complex adaptive
system, learning system, autopoietic system. In the evolution of metaphors of organization an
interesting phenomenon could be observed. The source fields of the first metaphors - machine and
organism were "external" to the organization. The source fields of open system, complex adaptive system,
autopoietic system and learning organization are overlapping with the concepts of organization itself
(the target field). The metaphors of organization can be then divided into two groups, following
specificity of their source fields - external (machine and biological system) and overlapping - the
remaining ones with varying level of the overlap. It is commonly agreed that the ability of
self-observation and knowledge about itself is an important part of an organization. For the "external"
metaphors, the self-referential mechanism is easier to identify. For the "overlapping" metaphors this
phenomenon has been studied predominantly for "second order cyberneticsand for autopoiesis of social
systems proposed by Niklas Luhmann. The main aim of the paper, which is an introduction to further
research, is to study how the evolution of application of metaphors has influenced the theory
organization. The following interpretations of analogies and metaphors will be applied: descriptive,
explanatory, predictive, normative, prescriptive and regulatory. Specific features of all metaphors of
organization will be described. It will be studied how "overlap" of the source field of metaphor with
the target field (organization itself) influences each of those approaches. For the more "overlapping"
metaphors a specific impact of development of the Information Society can be observed. From many facets
of the Information Society the following one will be exposed. The IS will be characterized as a system
with growing capability of mapping itself onto itself. This general observation will be preliminary
developed in the paper. Initial results of research referring to theory of organization and some
consequences of the phenomenon of "overlapping" of metaphors of organization for contemporary
microeconomics will be presented. The hypothesis will be put before that the proposed approach can be
helpful in introducing into the methodology of neoclassical economics the cognitive aspects, which in
turn, may prove helpful in deepened understanding of the "New Economy". The concepts of utility and
contractual approach in microeconomics will be the subject of introductory studies.

How Complex Systems Studies Could Help in Identification and Prevention of Threats of Terrorism

 The most dangerous threats were usually unpredictable, not because of objective barriers of their
predictability, but because of the social context which contributed to distortions of perceptions and
predictions by most of the actors involved. The main aim of the paper is to present a survey of
possibilities how complex systems studies can even partly contribute to identification and prevention of
the threat of terrorism. A broadly defined prediction is the core issue in any security discussions.
Identification of a threat should make possible subsequent future actions. Therefore attention will be
paid how complex systems studies can help in identification and subsequent prevention of threats. In
order to develop analytical properties of the concept of security, an eclectic approach will be
proposed. Its purpose is to combine objective value of widened neorealist broadened security concept
with the constructivist and at the same time deepened idea of security viewed as an;act of speech (Buzan
et al. 1998). (References will be provided in the paper) In the eclectic approach security is referred
to the following sectors: military, economic, political, environmental and societal and the concepts of
existential threat, securitization and desecuritization are used - Buzan et al. (1998). Some of the
founders of systems thinking were also involved in security-related studies - peace research (Anatol
Rapoport, Kenneth Boulding), international relations - Karl W. Deutsch (1964), Morton Kaplan (1957).
Similarly many modern works on security expose the links with systems thinking and complexity studies -
direct references - (Rosenau 1990, 1997), (Snyder & Jervis 1993), and indirect introductory references
(Kauffman 1993, 1995). Other, more specific applications can be also found, e.g. a book edited by
Alberts and Czerwinski (1999), works by RAND Corporation (RAND Workshop 2000). In searching the links
between complex systems studies and threats of terrorism the following problems seem most important: 1.
Effective identification of threats of terrorism. 2. Securitization and desecuritization of threats of
terrorism. 3. Methods of prediction of terrorism - strategic and operational, day-to-day basis. The
survey of potential applications of complex systems studies in identification and prevention of
terrorism will include two levels: First, applications of analogies and metaphors deriving from systems
thinking and complexity studies in the language of security theory and policy. In applications of
models, analogies and metaphors the following approaches can be identified: descriptive, explanatory,
predictive, normative, prescriptive and regulatory. Since it can be agreed that security is a product of
the social discourse (securitization), it is necessary to answer how the ideas drawn from systems
thinking and complexity studies can be used in all aspects of securitization - identification of threats
of terrorism and in their prevention. Second, assessment of possibilities to build mathematical models
helpful in identification and prevention of threats, especially including threat of terrorism. In this
point an attempt will be made to provide and answer to the question if some areas of complex systems
studies could prospectively become similar as game theory for conflict studies. 


H. N. Mhaskar - California State University, Los Angeles.

When is approximation by Gaussian networks necessarily a linear process?

 An important characteristics of a Gaussian network is the minimal separation among its centers. For
example, the stability of interpolation and the degree of approximation is often estimated in terms of
this minimal separation. We prove that for a target function decaying at a polynomial rate near
infinity, the degree of approximation of the function by Gaussian networks decays polynomially in terms
of the minimal separation if and only if linear approximation processes from the theory of weighted
polynomial approximation provide a comparable degree of approximation. We describe constructions of
Gaussian networks with an optimal degree of approximation measured in terms of the minimal separation as
well as the number of neurons, where the coefficients are linear functionals of the target function and
the centers are fixed, independent of the target function. The coefficients can also be constructed
using samples of the function at randomly selected sites. 

 Micah Sparacio -
Temple University

The Role of Information in the Organization of Complex Systems

 In this paper we explore the role that information plays in the organization of mass-energy into
complex systems. Specifically, we focus on the information flow that occurs when low-entropy information
plays a direct role in the reduction of entropy of a physical system. While complexity and
self-organizational theorists tend to focus on the role of energy flow from an energy source to an
energy sink to explain the organization of complex systems, we hope to highlight the often overlooked,
but crucial role that information plays in this process. In order to accomplish this, we will first
present a definition of information that is richer than traditional Shannon-Weaver theory and
incorporates a sense of functionality and what we call "signal agreement". We wll then demonstrate the
importance of this type of information in the organization of complex systems, beginning with Maxwell's
Demon paradox and then applying the principles to genetic algorithms and biologcal systems. We find that
the universe can best be modeled as an information-responsive system, in which complex systems are the
secondary effect of various information pathways. Thus, we propose that the study of complex systems
should emphasize the crucial role of information and develop a method of detailing the origins and flow
of information. 

 Juan Carlos Micó - Universitat Politècnica de València

Continuous Space-Time Dynamics of Population. Juan Carlos Micó, Antonio Caselles, David Soler

 The aim of this paper is to present a new formal approach, based on an integral-differential equation,
the Space-Time State Transition Equation (STSTE), from which we can build space-time dynamical models of
complex systems. The STSTE provides the partial derivative of the density of a state-variable with
respect to time as a sum of time rates and space-time rates. The time rates describe the dynamics of the
system for each space-point, independently of the other points, whilst space-time rates describe this
evolution as a consequence of the relation of each space-point with a given set of other points. This
relation contains integrals over the accessibility domains (sets of space-points with which each
space-point is related). The STSTE is provided for any system of space-coordinates and is compared with
other approaches written in finite differences form, pointing out why the approach given by the STSTE is
better than the finite differences one. Finally, we present an application case for Spain, performing a
Visual C++ 6.0 program for the situation, in order to obtain numerical results, which are finally
commented. 

 Daniel W. Miller - Greenwich Univesity

The Complexity of Homeodynamic Psychophysiological Systems

 Mind and body, as well as the external environment, are homeodynamic, complex systems. The homeodynamic
aspect is vital to the maintenance of connectivity and optimal functioning in non-equilibrium states.
Mind and body are each in a continual state of flux and change. They areenergetically different
mechanisms interacting within the necessities of survival dynamics. The homeodynamic concept
demonstrates clearly that without its interactions the organism could not survive. It is functionally
inherited with DNA and operates through cellular, hormonal and neural processes such as stem cells and
the neuroimmunological system. There also are many reports of primitive bacteria as well as higher
organisms that depend on homeodynamic processes for survival. These indicate that homeodynamic processes
are participants in evolutionary development. The position is taken that the effectiveness of random
mutation in evolution is secondary to the more powerful intervention of directed evolution. Furthermore,
in conjunction with directed evolution, support can be found for the development of neural consciousness
from primitive structures to the complexity of human capabilities. Vital questions need to be discussed,
such as how might body and mind interact in the human organism if they are energetically different
mechanisms and how can dysfunctional states of body and mind be clarified through homeodynamic
explanations? Is there a need to include spiritual systems in scientific theory? An attempt will be made
to answer these and other issues. 

 Boris Mitavskiy - University of Michigan
in Ann Arbor

The Universality of a Slightly Generalized Binary Genetic Algorithm

 I will set up a mathematical framework to invastigate the structure of invariant sets under the family
of the crossover operators for a genetic algorithm. Then I shall demonstrate how this machinary allows
us to prove that, in a certain sence, under surprisingly weak equirements, every heuristic search
algorithm is isomorphic to a binary genetic algorithm with possibly a nonuniform distribution on the
family of mutation operators. I'll also show a number of other facts such as how to modify a genetic
algorithm so as to minimize the role of mutation while preserving the ergodicity of the algorithm. 

 Olga Mitina - Moscow State University

The Perception of Fractals: The Construction of Psychometric Function of Complexity and Correlation With
Personal Traits.

 The experimental research for construction of functional relation between objective parameters of
fractals' complexity (fractal dimension and Lyapunov exponent) and subjective perception of their
complexity was conducted. As stimulus material we used the program created by F.Abraham, based on
Sprott's algorithms of generation of fractals and calculation of their mathematical characteristics. For
the research 20 fractals were selected which had different fractal dimensions in a range from 1 up to 2.
We conducted 3 series of experiments. 1. 20 fractals were showed to subjects (100 persons). The pictures
of the fractals were formed on the screen of the computer during randomly chosen time interval in the
definite range (from 5 till 20 sec). For every fractal the subject should give the answers about his
(her) opinion about the complexity and attractiveness of the fractal using ten-point scale. Each subject
also answered the questions of some personality tests (Kettel and other). The main purpose of this
series was the analysis of correlation between personal characteristics of respondents and their
subjective perception of complexity, attractiveness and time of fractal's presentation. 2. The same 20
fractals were showed to subjects (50 persons), but they were forming on the screen of the computer
during the same time interval (for all fractals and all subjects). Subjects also estimated subjective
complexity and attractiveness of fractals. The given series was necessary for revealing what depends on
concrete fractal when a subject answers the questions about the complexity and attractiveness in the
fixed time interval of presentation. 3. The same 20 fractals were showed to subjects (30 persons), but
in the random order and ten times for each. Thus each experiment included 200 presentations. For every
fractal psychometric functions of objective parameters of complexity and it's subjective value by each
respondent were constructed. On the basis of the conducted research at the department of psychology of
Moscow State University the special practical course was developed where students learn about fractals
and do practical work on evaluating specific fractals and construct psychometric curves of time and
complexity perception. 

 Josh Mitteldorf - U of Pennsylvania

Demographic Homeostasis and the Evolution of Senescence

 Evolution of senescence has been explained in terms of pleiotropy and tradeoffs between longevity and
reproduction. Such theories fit comfortably within a population genetic framework where individual
reproductive potential is assumed to be optimized under natural selection. We have speculated that
demographic homeostasis may be a driving force in evolution that rivals reproductive potential in its
force and ubiquity. This hypothesis may support very different theories for the evolution of senescence.
Diverse experimental evidence from field studies and genetics laboratories suggests that senescence is
broadly regulated under genetic control. Only the strength of population genetic theory has prevented
the inference that senescence is an evolutionary adaptation. A broader theoretical framework, in which
fitness of populations and ecosystems is regarded as a substantial counterweight to individual fitness,
permits this inference and suggests mechanisms by which senescence may be selected for its own sake. We
summarize three models in which senescence may evolve based on benefits to the population that outweigh
its cost to the individual. The first model is based simply on logistic growth. Iteration of the
logistic equation was, historically, one of the earliest examples through which the mathematical notion
of chaos was studied. When the logistic equation is iterated with too large a timestep, chaotic
population dynamics ensue. We show how lifespan can act like a finite timestep, delaying the feedback of
crowding on population growth; thus limits on lifespan may evolve for the purpose of suppressing
demographic chaos. The second model illustrates coevolution, in which predator/prey population dynamics
are stabilized by the difference in vulnerability between old and young prey. In these two cases,
senescence contributes directly to demographic stability; but we have observed other models (and other
parameter sets within these same models) in which this is not the case. Indeed, senescence may be
stabilizing or destabilizing to population dynamics. Nevertheless, a context in which individual
reproductive potential is not maximized profoundly alters the landscape in which senescence must evolve,
enhancing the plausibility of other mechanisms by which senescence may be selected for its own sake. The
third model illustrates the possibility that senescence may evolve based upon benefits to the population
which are not directly related to demographic stability. These benefits include population diversity and
a shorter effective generation cycle. In a context dominated by selection pressure for optimal
reproductive potential, these benefits are insufficient to overcome the individual costs; however once
individual reproductive potential is subject to stabilizing selection (for reasons unrelated to
senescence) a window opens for senescence to evolve in association with enhanced fertility. This third
mechanism may be easily confused with pleiotropy. The difference is that in pleiotropy, reproductive
potential alone is maximized, but some genes with a benefit for fertility impose an incidental cost,
identified with senescence. In the new concept, fertility and longevity are governed by independent
genes, but they are constrained to evolve together because the combination of high fertility with long
lifespan leads to demographic chaos. 

 Josh Mitteldorf

On The Prudent Predator. Josh Mitteldorf, Chandu Ravela, Robert Bell, Dominic L. Boccelli, David H.
Croll, Deva Seetharam

 Reproductive restraint can dampen demographic oscillations that might otherwise be lethal to a
population. In the past, Wynne-Edwards (among others) surveyed many natural examples of this phenomenon,
and speculated that population regulation might be a ubiquitous evolutionary adaptation. However, a
commonly held view is premised on the maximization of individual fitness (as measured by reproductive
value) and, thus, the theoretical question of whether populations may moderate their fertility in order
to stabilize growth of the food species on which they depend was thought to have been settled in the
negative three decades ago. We present a surprisingly simple, individual-based evolutionary model in
defiance of this conventional wisdom. Predator and prey populations occupy sites on a viscous grid.
Probabilities for reproduction and death of individual predators and prey are derived from a dampened
Lotka-Volterra dynamic. Predators evolve with a single gene ("alpha-gene") that governs their propensity
both to consume prey, and to reproduce. We have identified a range of parameter values for which
predators evolve to a moderate level of alpha-gene which may persist indefinitely within a narrow range.
The evolutionary mechanism seems to be that grid sites in which the alpha-gene evolves too high a level
experience chaotic population dynamics which drive the prey, and hence the predators, to local
extinction. The site is then re-colonized by migrants from neighboring sites where the alpha-gene has
not yet achieved such a high value. We speculate that if further investigation reveals this result to be
as general as Wynne-Edwards once supposed, then the consequences for the theoretical science of
evolution may be profound. We may come to think of fitness not in terms of individual reproductive
potential, but of stable ecosystems. 

 Mihnea Moldoveanu - University of
Toronto

How Does the Mind Economize on Complexity Costs? A Principal-Agent Model of Affect and Cognition ?

 The 'complexity' of a phenomenon is modeled as the result of an interaction between a cognitive schema
and a set of observations that corroborate or refute predictions deductively derived from the schema.
The paper attempts to provide an economic model of the process by which various schemata are chosen. the
model is based on a partitioning between a cognitive function or agency (supplying theories, models,
predictions and ascertaining their degree of corroboration) and an affective function (supplying
selective rewards (such as brain blood glucose levels) to the cognitive function on the basis of the
relative success of the predictions in question). Affect rewards cognition in proportion to the accuracy
of the predictions in question. The model is shown to have a unique optimal contracting solution. This
solution corresponds precisely to a propensity of decision makers to minimize uncertainty, defined in
the sense of Shannon entropy. The model is used to (a) explain well-known results from cognitive
psychology regarding cognitive 'biases' and 'fallacies' and to link these results to the neurophysiology
of fear, anxiety and responses to anomaly (which are interpreted as failed predictions).

The Economics of Cognition

 Dealing with complexity is informationally and computationally intensive. Cognitive pragmatism often
requires that decision making agents make choices not only about choosable options, but also about the
models that they use in order to represent a certain situation. The paper builds a model of the ways in
which cognitive pragmatists choose among various models, and presents applications to the understanding
of complex organizational phenomena and industry dynamics. The model is based on a categorization of
different representations and models in terms of the computational complexity of the algorithms required
to competently simulate a phenomenon or make predictions about using a partiucular model. The utility of
a model to the decision maker is measured in terms of (a) the value of precision in a problem-solving
scenario, (b) the cost to the decision maker of performing additional computations, and (c) the
information produced by each iteration of an algorithm based on the decision maker's model. The paper
arrives at a model of cognitive pragmatism that makes predictions about which models (of organizations,
industries, individuals) are most likely to propagate forward in different market or social niches. 

 Young B. Moon - Syracuse University

A Near Optimal Policy to Control Multi-Product, Multi-Stage Manufacturing Systems

 Heuristic manufacturing control policies such as MRP (Materials Requirements Planning), Kanban, CONWIP
(Constant Work In Process) and other hybridpolicies have been employed to execute production plans at a
manufacturing shop floor. In practice, the complexity of the problem has demanded aselection of a
control policy and a subsequent determination of control parameters. For example, the decision
parameters such as the number ofkanbans, job sequences, lot sizes, input control schemes, dispatching
rules and others are determined after the CONWIP control policy is chosenfor a flow line. Studies have
been done for determining optimal set of decision parameters for a given control policy, but only for
simple flowline systems. Extensions to more complex manufacturing systems have been difficult due to the
enormous size of state space to deal with. Thisresearch presents a new approach that considers the
issues of determining decision parameters and a control policy concurrently. The approachadopts a
modified Reinforcement Learning algorithm called Semi-Markov Average Reward Technique (SMART). The
control system has been tested formulti-product, multi-stage manufacturing systems and generated
promising results. The developed approach also provides a simulation environment to evaluate various
control policies for a manufacturing system. The consideredcontrol policies are Kanban, CONWIP, Extended
Kanban Control System (EKCS), and the Behavior-Based Control (BBC) policies. However, other
controlpolicies can be easily added to the list. Initial evaluation of the approach has been done with
two major performance measures: the service level (or fill rate) and the amount ofwork-in-process
inventory in the system. The service level is the fraction of all demands that find a component ready
for use when a demandoccurs. We define the work-in-process inventory as the amount of material that has
been loaded on the first machine but has not yet been deliveredto satisfy the demand. Using the
approach, we are also able to observe the relationships between the service level and inventory for
differentcontrol policies. The assumptions and characteristics of manufacturing systems considered are
as follows: The first stage has sufficient amount of raw material towork on for each product and Mth
stage is followed by a finite capacity warehouse where the finished products are stored. The demand for
afinished product follows a compound Poisson process. The demand size is assume to be an arbitrarily
distributed random variable. Demand of aproduct is immediately satisfied upon arrival, if there is
sufficient amount of inventory on-hand. Any unsatisfied portion of the demand isassumed to be
backordered. The products are produced one by one and each product has to pass through all the stage of
the system in the order inwhich the stages are arranged. At any stage, the products are differentiated
from each other on the basis of priorities that may be different fromone stage to another. Due to the
existence of set-ups, it is preferable to use production control policy that avoids too many switching
among theproducts. The inventory level as well as the manufacturing of a product at a given stage are
controlled through the use of a ReinforcementLearning agent. 

 Mark Musolino -
University of Pittsburgh

Complexity of Musical Measures and its Effect on Time Perception 

 Radhika
Nagpal - MIT

Programmable Self-Assembly and Scale-Independence

 Cells cooperate to form complex structures, such as ourselves, with incredible reliability and
precision in the face of constantly dying and replacing parts. Emerging technologies, such as MEMs, are
making it possible to embed millions of tiny computing and sensing devices into materials and the
environment. We would like to be able to build novel applications from these technologies, such as smart
materials and reconfigurable robots, that achieve the kind of complexity and reliability that cells
achieve. These new environments pose significant challenges: a) How does one achieve a particular global
goal from the purely local interactions of vast numbers of parts? b) What are the appropriate local and
global programming paradigms for engineering such systems? In this talk, I will present an example of
how programmable self-assembly can be achieved, from the local interactions of identically-programmed
agents ("cells") connected in a sheet. The approach is significantly different from current approaches
to emergent systems: the desired global shape is described at an "abstract" level, using a language
based on a set of geometry axioms, which is then {\em automatically compiled} into a program run by
individual cells. The cell program itself is composed from a small set of local organization primitives,
inspired by studies of developmental biology. The resulting process is extremely reliable in the face of
random cell distributions, varying cell numbers, and random cell death, without relying on global
coordinates or centralized control. A wide variety of folded shapes and 2D patterns can be specified at
an abstract level, and then synthesized from the collective behavior of identically-programmed cells.
The programmable self-assembly provides many robust mechanisms that are applicable to programming smart
matter applications. The process also exhibits several structural traits that have strong parallels to
those seen in biological systems. In this talk, I will focus on one particular property, called {\em
scale-independence}. Scale-independence implies that a cell program can create the same shape/pattern at
many different scales, without any modification. The shape scales automatically with the size of the
sheet and the total number of cells. The shape even scales asymmetrically with the initial shape of the
sheet, allowing a single program to generate many related shapes, such as D'Arcy Thompson's famous
coordinate transformations which he used to explain shape differences in related species (in ``On Growth
and Form''). Scale-independence is common in biology; sea urchin and hydra embryos develop normally over
ten-fold size differences, and morphologically distinct species of Drosophila exist with practically
identical DNA. Genetic analysis is unlikely to reveal much information in such cases. Our artificial
system gives us insights into how such shapes could be produced at the cell level by a single program,
and we plan to exploit these insights to design biological experiments. We believe that the results from
this research will have an impact on our engineering principles for robust design as well as our
understanding of biological morphogenesis. 

 MJ Naidoo - Kingshill Research

 Many health service users and providers are set in their ways. Approaching the task of implementing
service improvements in Dementia Services using creativity and complexity science the team at Kingshill
were able to demonstrate that by trusting in the process of change and allowing the experience of
emergent and novel behaviour to form new perceptions and a greater dynamic in quality improvement was
established. This involved a more coherent integrated service that challenges much of the old behaviour
and relationships among all stakeholders. By facilitating all groups in the process and encouraging
emergent behaviour using techniques derived from the creative arts the team at Kingshill witnessed
transitional phases involved in the development of novel behaviour and new team led dynamics that
focussed on implementing improved quality systems and more meaningful relationships between service
users and providers. Using practical creative exercises and storytelling also demonstrated the powerful
contribution made by carers and patients in treatment of this disease. By enhancing the quality of
communication and understanding that emerged from the use of creative storytelling techniques, decisions
relating to care, both clinical and non clinical, contributed to the implementation of an integrated
service for patients. For all the stakeholders involved in the project the process of continual
transformation and development had begun, a key feature of complexity science, and a meaningful exemplar
for other teams and organisations a 

 Vidyardhi Nanduri - Cosmology
Research Centre, SYTE P/L

An Approach to Cosmos Complex Structure 

 Douglas E. Norton - Villanova
University

Epsilon-Pseudo-Orbits and Applications

 Many aspects of research in Dynamical Systems, both continuous and discrete, have involved computer
modeling. One recent trend in this direction is for inherently quantitative techniques to yield
qualitative information about the systems under investigation; for example, see various papers in
Kloeden (1). McGehee in (2) and the current author in (3-5) investigate epsilon-approximations for
discrete dynamical systems and the implications for observability of attractors and other sets defined
by the dynamics. In particular, the Conley Decomposition of a space can be approximated by an
epsilon-coarse Conley Decomposition. psilon-pseudo-orbits yield models not only of computer models
themselves but also models of real-world phenomena in which the epsilon-jumps are key ingredients of the
behavior of the system. One such system is neural activity of the brain. Basic results on
epsilon-pseudo-orbits and some applications will be presented. 1. Kloeden, P.E. and K.J. Palmer, eds.,
Chaotic Numerics, Vol. 172 in Contemporary Mathematics, AMS, 1994. 2. McGehee, R.P., Some Metric
Properties of Attractors with Applications to Computer Simulations of Dynamical Systems, 1988, preprint.
3. Norton, D.E. "The Fundamental Theorem of Dynamical Systems," Commentationes Mathematicae
Universitatis Carolinae, Volume 36, Number 3, 1995, pages 585-597. 4. Norton, D.E. "The Conley
Decomposition Theorem for Maps: A Metric Approach," Commentarii Mathematici Universitatis Sancti Pauli,
Volume 44, Number 2, 1995, pages 151-173. 5. Norton, D.E. "Coarse-Grain Dynamics and the Conley
Decomposition Theorem," to appear in Mathematical and Computer Modelling. 


Celestine A. Ntuen - North Carolina A&T State University

Human Interaction With Complex Automation: A Summary of Symposia Outcomes

 In this talk, I present a summary of observations with respect to human interaction with complex
systems of automation and informationtechnology.The transcendental, phenomenal, and instrumental aspects
of designing human-system interaction tools are elucidated. In addition,I present the components of
complexity in designing adaptive and intelligent interfaces that cope with agents and cooperative
systems. Observationsfrom expert positions on the subject matter based on six symposia on "Human
Interaction with Complex Systems" are analyzed and compared. 

 Kenneth C.
OBrien - OPEN Strategies

Common Sense and Complexity: Harnessing the Science of Complexity for Your Business

 The scientific community is continuing to explore the implications of the science of complexity and
complex systems. While there is much yet to be done, concepts are emerging that appear to have immediate
application to organizations and business. Some members of the consulting community have started
integrating selected elements of their interpretation of the science in their work with clients. It is
crucial that the scientific and consulting communities maintain a robust dialog during this
developmental period to insure there is continuity between the efforts. To that end, this presentation
provides an overview of the work being done by one consultant to communicate the significance of the
science and how it can be applied to business. To frame it another way, it's about "how the world works
according to the science of complexity and what it might mean to a business". The presentation will
cover the concept of organizational fitness as a strategic planning framework/context to integrate the
implications of the science into an organization's planning process. This consulting approach has been
used with a number of clients including energy utilities, governmental entities and the University of
Washington Medical Center. 

 Sorinel Oprisan - University of New Orleans

Assessing the Qualitative Dynamics in Emergent Behavior Via Quantitative Features: An Application to
Swarm Intelligence Model.

 Autonomous mobile robots exhibiting cooperative behavior e merged as a distinct research field among
the artificial intelligent applications during the last two decades. Most of the models in emergent
behavior are biologically inspired by way of social insects behavior. From their local perception to the
mass effect that results in a global action these biological systems serve to elucidate the mechanisms
thought to be at the heart of self-organizing behavior, sometimes called stigmergic self-organization or
swarm intelligence. The purpose of modeling multiagent systems is twofold. Firstly, it leads to a deeper
understanding of social insects behavior. Secondly, the studies provide a decentralized, efficient,
approach on robots task allocation. The motivation of our studies is twofold. First, there is a
noticeable gap in the literature of cooperative robots regarding formal metrics for cooperation and
system performance. While the notion of robots cooperation is difficult to formalize, such metrics will
be very useful in characterizing the nature of agent interactions. Second, experimental studies might
become more rigorous and thorough, e.g., via standard benchmark problems and algorithms. We proved, by
the way of numerical simulations, that the theoretically derived values of the feature are reliable
measures of aggregation degree. The slop of the features dependence on memory radius leads to an
optimization criterion for stochastic functional self-organization. We also described the intellectual
heritages that have guided our research, as well as possible future developments. On the other hand,
using a swarm of robots inspired from social insects behavior has some drawbacks. Stagnation is one of
the major problems: because of the lack of a global knowledge, a group of robots may find itself in a
deadlock, where it cannot make any progress. Another problem is to determine how these so-called
"simple" robots should be programmed to perform user-designed tasks. The pathways to solutions are
usually not predefined but emergent, and solving a problem amounts to finding a trajectory for the
system and its environment so that the states of both the system and the environment constitute the
solution to the problem: although appealing, this formulation does not lend itself to easy programming.
Until now, we implicitly assumed that all robots were identical units: the situation becomes more
complicated when the robots have different characteristics, respond to different stimuli, or respond
differently to the same stimuli, and so forth; if the body of theory that roboticists can use for
homogeneous groups of robots is limited, there is virtually no theoretical guideline for the emergent
design and control of heterogeneous swarms. 

 Belinda Orme - Icosystem

Chaos and Mixing in Biological Fluids

 The motion of particles and feeding currents created by micro-organisms due to a beating flagellum are
considered. The calculations are pertinent to a range of sessile organisms, but we concentrate on a
particular organism, Salpingoeca Amphoridium (SA)(a choanoflagellate); due to the availability of
experimental data, Pettitt (2000). These flow fields are characterised as having very small Reynolds
numbers, implying viscous forces dominate over inertial ones consistent with the Stokes flow limit. The
flow generated by the flagella is initially modelled via consideration of a point force known as a
stokeslet. The interaction between the boundary to which the organism is attached and its flagellum
leads to toroidal eddies, which serve to transport particles towards the micro-organism; promoting
filtering of nutrients by SA. It is our conjecture that the interaction of multiple toroidal eddies will
lead to chaotic advection and hence enhance the domain of feeding for these organisms. We illustrate the
degree of mixing in the region around SA using chaotic measures to study the influence the flagellum has
on the surrounding fluid. Poincare sections illustrate a sense of chaotic mixing whilst Lyapunov
exponents characterize how well mixed the fluid becomes. Further, three-dimensional particle paths
around such an organism are considered using Greens functions. Comparisons of flow patterns from
numerical data with both experimental and theoretical work suggest agreement. 

 Jaime Lagunez Otero - UNAM

From Physics to Economy via Biology

 The cell is behaves has several emergent cognitive properties, in a way of a higher level than that of
simple CNS. We would like to present some examples and discuss the possibility of evaluating such
capacities. The systems that we study are in the process of cell growth and death, subjects relevant in
the study of oncogenic processes and aging. Our page requiring maintainance is at 132.248.11.4
interests: As you may remember, as I have attended NECSI events several times, I am PI for genetic and
proteic networks. We have explored several computational paradigms such as agent based programing in
order to search for emergent capacities within the signal transduction system of the cell. 

 N. Oztas - University of Southern California

Mapping the Field: Complexity Sciences in Organization and Management. N. Oztas, T. Huerta, R.C. Myrtle,
P. J. Robertson1

 The purpose of this paper is to provide an overview of the application of complexity sciences to the
organization and management theory literature. To address this issue, we searched the ABI/INFORM online
database, which provides access to over 1,000 premier worldwide business and management periodicals,
using self-organization chaos theory and complexity sciences as key words. Our initial search showed 519
pieces published between 1980 and 2000. After eliminating the non-management and non-complexity sciences
articles, our final sample consisted of 178 papers published in 105 different magazines and journals
between 1985 and 2000. To analyze these articles, we used Barley, Meyer, and Gash's (1988) original
triple clustering of types of academic literature -- descriptive, practice, and theory. We also added a
fourth group of studies from popular magazines and compared them to the former three clusters of
scholarly work. Our initial review of the application of complexity in these four clusters found that
emerging sciences of complexity have received an important amount of attention in both theory and
practice of organizational life. A characteristic common to all four groups of studies is that, because
the new sciences originate from the physical and natural sciences and still are evolving even in those
fields, the literature and the techniques are not yet fully developed or institutionalized either in
academia or professional life. We initially observed that, while one group of studies approached the
topic as an extension and refinement of existing theories, another group of studies has already started
proposing theoretical and empirical foundations for a completely new approach to organizational life,
not only by showing the applicability of complexity science approaches to organizational life as a new
paradigm, but also by establishing links with existing organizational literature. Additionally, a group
of professional managers and consultants has already started experimenting in their organizations with
novel approaches emerging from the new sciences of complexity. Despite these optimistic findings, our
preliminary results also support concerns regarding complexity theory's susceptibility to becoming
another fad in management practice (McKelvey, 1999; Maguire and McKelvey, 1999). In order to establish a
firm foundation for institutionalizing complexity sciences in the organization and management field,
there is an urgent need for additional conceptual clarification, theory development, and empirical
research. This paper constitutes the first step in analyzing these four groups of studies in an attempt
to map and aggregate the arguments of the emerging literature in the organization and management field.
In this initial analysis, we focus on the descriptive qualities of the publications to map the field and
leave the detailed focus on content for a subsequent paper. 1. School of Policy, Planning, and
Development University of Southern California, Los Angeles, California. 

 Lael
Parrott - Université de Montréal

Can Self-Organisation be Used as a Measure of Ecological Integrity?

 Self-organisation is a cardinal feature of all living systems, and, as early as the 1940's, E.
Shrödinger suggested that the phenomena might be a defining characteristic that distinguishes living
systems from non-living systems. Since then, numerous measures of self-organisation have been introduced
and been applied to all types of living systems. Most recently, self-organisation has been proposed as a
possible indicator of ecological integrity, with the hypothesis being that undisturbed ecosystems should
be more effective at utilising available energy, and should, therefore, have higher measures of
self-organisation. This hypothesis, as well as the general usefulness of measures of self-organisation
to characterise an ecosystem's state, is explored using an individual-oriented model of a materially
closed ecosystem. Results based on relatively simple ecosystem configurations show marked differences in
self-organisation between systems with different community structures. We conclude, therefore, that
measures of self-organisation may be useful indicators of ecological integrity. The idea will be pursued
further with field data from a Laurentian ecosystem. 

 Joel Peck - University of
Sussex

Sex and Altruism

 The evolution of sexual reproduction is one of the long standing mysteries in evolutionary biology. The
evolution of "altruistic" behaviour has also been a focus of much research. In this talk some of the
difficulties surrounding the theories of sex and altruism will be explained. A solution will then be
proposed that may help to resolve the mysteries surrounding both sexual reproduction, and the evolution
of altruistic behaviour. 

 David W. Peterson - Ventana Systems, Inc.

An Adaptive Endogenous Theory of Technological and Economic Growth

 Long-term per capita real economic growth is driven by technological progress, which is exogenous to
most theories. A new agent-based model shows that the empirically observed patterns of economic growth
of nations emerge naturally from the coevolution of many mutually interacting firms. The model includes
simple rules for the founding of firms, their strategic decisions for allocating internal resources,
their acquisition of external resources, their competitive interaction in the market, their decisions of
when and how much to change their policies, and their shutdown, acquisition, or breakup in case of
failure. Simulations of the model reveal long-term trends qualitatively matching the observed data, both
in growth trends and in observed patterns of size-convergence and "glass ceiling" effects seen in
developing nations. The model also suggests some new root causes of the observed differences in economic
growth rates among nations. Finally, the model suggests that the growth patterns observed to be stable
over the last 200 years are in fact only metastable. With the right kind of perturbation, the patterns
of economic growth could bifurcate suddenly into a new, dynamically stable, mode. 


O. A. Petrov - Saint-Petersburg State University

Synergetics of Living Systems

 Recent interest to the complex systems analysis in living matter results from the obvious progress in
self-organization theory, as well as from increasingly distinctness understanding that this
interdisciplinary approach give the keys to solution of a number important biological problems.
Unfortunately, in most cases, in the text-books the main idea was implemented that the specificity in
studying of self-organizing living systems consist in the appropriate interpretation of equations -
mathematical models of the processes. Synergetics is frequently identified with the non-linear dynamics
with the emphasis on the detailed investigation the basic mathematical background. In this context, we
would like to underline, that among a great number of a self-organizing processes in living matter there
are some ones, that demonstrate a qualitative distinction between living and similar non-living
self-organizing systems. Here, we imply, first of all, those processes that define the purposeful
behaviour and activity of the living systems in its surroundings. Self-organization take place in this
case according with the emergent purpose (need) inside living system (for instance, satifaction of
necessities of life). To our opinion, during teaching biologists to the self-organizing living systems,
it is important not only explain the basic concepts of self-organization theory. These concepts should
be turned to explanation the primary regulation of vital function of the whole organisms, formation of
their behaviour in the environment. On the basis of such approach the course for the students-biologists
was elaborated. 

 Christopher J. Phoenix

A Multi-Level Synthesis of Dyslexia

 Dyslexia has been studied from many angles. Researchers have obtained seemingly contradictory results
and created widely varying theories and treatments. A complete understanding of dyslexia requires
recognition of neurological and psychological components and their interaction, and could therefore
benefit from a complex systems approach. This paper surveys and synthesizes results from many
theoretical, experimental, and clinical approaches to dyslexia, including Tallal, Davis, Geiger, and
Merzenich. The magnocellular hypothesis combined with the Davis theory of "triggers" appear to explain
nearly every experimental result, observation, and successful treatment of which the author is aware.
Dyslexia can be understood as an accretion of simple symptoms in multiple sensory modalities, each
symptom having the same neurological basis; each individual has a different combination of symptoms, and
the symptoms are created and maintained through mental/psychological interaction with the individual's
efforts to perform. There is strong observational evidence, confirmed by pilot studies carried out by
the author, that the symptoms can change momentarily. Although such rapid change is not recognized by
many dyslexia researchers, it has been demonstrated with PET scans in the case of stuttering; this
finding is crucial to a full understanding of the interaction between neural function and mental state.
The recognition of the diversity of symptoms, their common neurological basis, and their extreme
plasticity in response to high-level mental state, may help to focus research and to develop
increasingly effective and rapid treatments.

Control and Complexity Issues in a Proposed Nanomedical Device

 This paper addresses control and complexity issues in a proposed medical device performing a
straightforward yet extremely complex function. In a few decades, devices may become far more intricate
than software is today; the science of complexity will become as necessary to successful design as the
science of materials. This proposal supplies a preview of the problems that complexity scientists may be
called upon to solve. The purpose of the proposed device is to replace blood by lining blood vessels and
performing transport functions. Molecular nanotechnology (MNT) promises the capability of building such
a device; however, its design is far beyond our present abilities. The device contains 3x10^14
cooperating machines, with over 2x10^11 MIPS of distributed computing. It must maintain appropriate
concentrations of all chemicals in all areas of the body, and support the immune system by delivering
white cells as necessary. Although incredibly aggressive by today's standards, the medical benefits
could make it a desirable product if the design problems can be solved. Such a project seems fantastic,
but it should not be viewed in isolation--several proposed products of MNT are equally complex. MNT
promises access to unprecedented capability and complexity of devices, and product designers will surely
push the envelope. Successful designs will require significant advances in distributed computing,
medical systems, fault detection and recovery, and control of complex feedback loops. The present
proposal may serve to inspire or focus efforts on a typical near-future design problem. 

 Sebastian Popescu - Laboratory of Complex Science

On the Mystery of the Differential Negative Resistance. S. Popescu, E. Lozneanu and M. Sanduloviciu

 As known, nonlinear phenomena emphasized when a system is driven away from thermodynamic equilibrium
can be observed in many cases investigated in physical, chemical and biological sciences. Informative
for the genuine origin of the nonlinear behavior are experimental investigations recently performed on
gaseous conductors [1,2]. Thus, when the gaseous conductor (plasma) is gradually driven away from the
thermodynamic equilibrium the system reveals two different bistable behaviors (bifurcations) that
successively appear in the form of an S-shaped, respectively an N-shaped negative differential
resistance (NDR). Their appearance signifies the development of two level of self-organization [1,2].
Thus, the S-shaped DNR has its origin in the creation, by self-organization, of a stationary complexity,
the self-consistence of which is ensured by an electrical double layer (DL). The N-shaped DNR appears
when this complexity transits into a new state during which the DL from its border sustains a rhythmic
exchange of matter and energy between the complexity and the surrounding environment. The basic
phenomenon that initiates the evolution towards the aforementioned two level of self-organization is a
local self-enhancing mechanism of the production of positive ions complemented by the creation of a net
negative space charge that, acting as a long-range inhibitor, explains the self-assembling of the DL
[2]. Usually the S-shaped NDR is associated with the self-assembling, in front of the anode, of a stable
complex space charge configuration bordered by a DL. Its genesis involves accumulation of charged
particles and electric field energy, both of them provided by the external power supply. Since the
self-assembling process of the complexity sensitively depends on the current, it becomes possible to
drive its formation and de-aggregation by changing the current. Therefore connecting a resonant system
to the system, it becomes possible to stimulate oscillations. Their existence is ensured by an internal
feedback mechanism by which the oscillations themselves drive the self-assembling and de-aggregation
processes of the complexity. The energy required for maintaining the oscillations is originated in the
complexity de-aggregation. It is related to the area of the hysteresis cycle that can be emphasized in
the static current-versus voltage characteristic when the anode voltage is gradually increased and
decreased [1]. Concerning the N-shaped NDR, this has its origin in the transition of the complexity,
formed in front of the anode, to a higher level of self-organization during which the complexity is able
to preserve its existence by a proper dynamics of the DL from its border. Under such conditions
spatiotemporal patters appear in the system, producing temporal variations of the current so that, in a
resonant system, suitable connected to the system, oscillations are stimulated. In this paper we
describe and compare pattern formation observed in semiconductors with those observed in gaseous
conductors. The observed similarities offer a new insight concerning the genuine origin of the observed
current-density instabilities in solid-state physics. Special attention is given to the Gunn effect, the
instability mechanism of which and pattern formation are far from being complete understood. REFERENCES:
1. E. Lozneanu, S. Popescu and M. Sanduloviciu, 3rd ICCS, Nashua, NH, USA 2000, InterJournal of Complex
Systems, article 357 (2001) http://www.interjournal.org 2. E. Lozneanu and M. Sanduloviciu, Int. Conf.
on Phenom in Ionized Gases 2001 Nagoya, Japan, Proceedings vol. 3 p 165. 


Carlos E. Puente - University of California, Davis

More Lessons From Complexity. The Origin: The Root of Peace

 The last few decades have witnessed the development of a host of ideas aimed at understanding and
predicting nature's ever present complexity. It is shown that such a work provides, through its detailed
study of order and disorder, a suitable framework for visualizing the dynamics and consequences of
mankind's ever present divisive traits. Specifically, this work explains how recent universal results
pertaining to the transition from order to chaos via a cascade of bifurcations point us to a serene
state of unselfish love, symbolized by the convergence to the origin in the root of a Feigenbaum's tree,
in which we all may achieve our inherently desired condition of justice, peace and joy. The implications
of these ideas regarding forgiveness and freedom are discussed.

Treasures Inside the Bell

 Universal constructions of univariate and bivariate Gaussian distributions, as transformations of
diffuse probability distributions via, respectively, plane- and space-filling fractal interpolating
functions, are reviewed. It is illustrated that the construction for the bivariate Gaussian distribution
yields infinite exotic kaleidoscopic decompositions of the bell in terms of beautiful "mandalas" having
arbitrary n-fold symmetry, for any n\>2.It is argued that these ideas yield a new paradigm for the
emergence of order, for a host of natural patterns, such as snow crystals and biochemical rosettes
(including life's own DNA), are found inside the bell. 

 Ruben R.
Puentedura - Bennington College

Slow and Steady: Deliberately Computationally Inefficient Genetic Algorithms and Hard Problems

It is known from the work of Axelrod, Miller, Lindgren and others that the application of genetic
algorithmic (GA) strategies to the Iterated Prisoner's Dilemma yields varying results, depending upon
the encoding used. In this paper I explore the results of using a new, "deliberately inefficient"
encoding for the GA that allows for the slow evolution of strategies of varying complexity, and develop
a quantification scheme to determine the degree of altruism of a given strategy. I find that, in
general, altruistic strategies do not carry the day; furthermore, the exact mix of strategies in the
long-term evolution of the system proves to be quite sensitive to both the error rate in the system (as
expected), as well as to the mutation rate in the GA. The results of this work indicate promising
possibilities for future applications of this encoding strategy to "hard" problems, such as protein
folding. 

 Hassan Qudrat-Ullah - National University of Singapore

Dynamic Decision Making and Learning in Complex Dynamic Environments

 Experimental research on dynamic decision making and learning in complex dynamic environments is
analyzed. Task performance and learning is seen as contingent on four major factors of a simulated
learning environment- decision maker, decision task, decision making environment, and the facilitator
support. Specifically, the research indicates that human-simulation and human-human interactions are
crucial to optimal performance and learning. A preliminary descriptive model of the impacts of these
interactions is constructed. 

 Derek Raine - University of Leicester

The Complexity Of Canonical Power Law Networks

 In the small-worlds networks (SW) of Watts and Strogatz the ratio of cliquishness to network diameter
C/L plays the role of a complexity parameter as a function of the rewiring probability of the network p.
The ratio C/L is small at the two extremes of order and randomness and large for intermediate
configurations. Networks that follow the node distribution of the standard canonical law (SCL) of
Mandelbrot have node frequency proportional to (n + m)b as a function of rank n, and interpolate between
the uniform random networks of Erdos and Renyi and the SW networks. In this paper we show how SCL
networks arise by maximising the information entropy subject to a fixed geometric mean node class, and
we investigate the extension of the complexity measure to these networks. We look at two examples of the
evolution of SLC networks. The first example is development during cell division. The second is the
fission-fusion scenario for the origin of life in which catalysis of polymerisation is non-specific. We
comment finally that these two examples can be distinguished in terms of the ‘languages’
recognised by the systems considered as automata. 

 Erik Rauch - MIT, NECSI

The Relationship Between Measures of Fitness and Time Scale in Evolution. Erik Rauch, Hiroki Sayama,
Charles Goodnight and Yaneer Bar-Yam

 The notion of fitness is central in evolutionary biology. It is widely assumed that the instantaneous
rate of change in frequency of a type is a valid measure of evolutionary fitness. We use a simple
spatially-extended predator-prey or host-pathogen model to show an important generic case where this
characterization fails. In the model, the evolutionarily stable type is out-competed in the short term
by seemingly fitter mutants. These mutants enjoy high reproduction ratios for many generations, but go
extinct in the long term (e.g. after 200 generations). The rapidly-reproducing types modify their local
environment in a way that is detrimental to their survival, but this environmental modification and its
feedback to population growth requires many generations. The distinct fates of the different types are
made possible by self-organized spatial segregation. The results imply that averages over space or time
should not always be assumed to adequately describe the evolutionary dynamics of spatially-distributed
ecological systems. We propose general quantitative measures of fitness that reflect the importance of
time scale in evolutionary processes, and also show how the results can be understood using the notion
of heritability of the environment. 

 Allan R. Robinson - Harvard University

Data Assimilation for Modeling and Predicting Multiscale Coupled Physical-Biological Dynamical
Interactions in the Sea Data assimilation is now being extended to interdisciplinary oceanography from
physical oceanography which has derived and extended methodologies from meteorology and engineering for
over a decade and a half. There is considerable potential for data assimilation to contribute powerfully
to understanding, modeling and predicting biological-physical interactions in the sea over the multiple
scales in time and space involved. However, the complexity and scope of the problem will require
substantial computational resources, adequate data sets, biological model developments and dedicated
novel assimilation algorithms. Interdisciplinary interactive processes, multiple temporal and spatial
scales, data and models of varied accuracies and simple to complex methods are discussed. The powerful
potential of dedicated compatible data sets is emphasized. Assimilation concepts and research issues are
overviewed and illustrated for both deep sea and coastal regions. Progress and prospectus in the areas
of parameter estimation, field estimation, models, data, errors and system evaluation arealso
summarized. 

 Luis Mateus Rocha - Los Alamos National Laboratory

Indirect Encoding of Phenotypes in Evolutionary Agents

 In this paper we present a simulation study of the evolutionary behavior of two different kinds of
agents. The first type of agents evolve via phenotypic variation or (Lamarkian) self-inspection, and the
second via genetic variation. Both types of agents possess self-organizing phenotypes, modeled by Fuzzy
Development Programs (FDP) [Rocha, 2001], which are procedures for combining fuzzy sets in such a way as
to model several characteristics of self-organizing development processes. The objective of the
simulation is to study the advantages of coded (genetic) reproduction over hypothetical, purely
dynamical alternatives, thus increasing our understanding of the origin of life problem. We show that
agents with genotypes tend to take over the environment in most conditions, except when high genotype
variation is present. We also discuss in detail the indirect encoding algorithm employed by the agents
with genotypes. These agents indirectly encode phenotypes by constructing them from available dynamics
building blocks given genetically encoded initial conditions for configurations of these building
blocks. This indirect encoding scheme models the process of development of phenotypes via
self-organization in real biology. Unlike traditional genetic algorithms, and the most radical theories
of neo-Darwinian evolution, genetic descriptions do not directly represent particular traits of
phenotypes. Rather, the final phenotypes are a result of non-linear self-organization, which is
irreversible and produces epistatic dependencies on phenotypic traits. The agents of the simulations
here presented possess these traits, which furthermore prove to be advantageous in applications of
genetic algorithms which we discuss. REFERENCES: Rocha, Luis M. [2001]. "Evolution with material symbol
systems". Biosystems. Vol. 60, pp. 95-121. 

 I. M. Rouzine - Tufts University

Realistic Model of Cellular Immune Response Against Human Immunodeficiency Virus. I. Rouzine and J.M.
Coffin.

 Background. Although many elements of immune system are well-studied experimentally, it is not known
how they are connected on the systemic level. Mathematical modeling of immune system as a network of
interacting cell compartments is an efficient way to obtain this information, and to design therapeutic
vaccines against viruses which persist in human body and are associated with high mortality, such as
HIV, hepatitis B and C. To choose the best model among the enormous number of biologically plausible
models, its predictions have to be compared with multiple quantities measured, at different time points,
for several experimental systems. In this work, we consider immune kinetics of HIV/SIV and the
lymphocytic choreomeningitis virus infecting mice (LCMV). Although virologically unrelated, the two
viruses show similarities at the systemic level of host-virus interaction. They both (i) persist in the
host at detectable levels, (ii) depend strongly on the presence of CD8 T cells, (iii) cause premature
depletion of antigen-specific CD8 T cells, and (iv) replicate in the lymphoid tissue cells. The
difference between the two viruses is that all the strains of HIV/SIV persist in vivo, while LCMV may or
may not persist depending on the strain and the initial doze. Development of efficient therapies against
such infections depends on understanding, on the systemic level, the scheme of interaction of different
cell types involved in the CTL response. Methods. Our starting hypothesis is that the mechanism of the
(impaired) CTL response is the same, on the systemic level, for HIV/SIV and LCMV and that the
differences between the two viruses and between LCMV strains are due to quantitative variation of system
parameters. We checked several dozen of models of primary CTL response (4 weeks) against data on 8
time-dependent immunological quantities obtained for SIV (Kuroda et al., 1999) and two strains of LCMV
with different behavior, Armstrong (Murali-Krishna et al., 1998) and Docile at low and high infecting
dozes (Moskophidis et al, 1993). Results. The simplest model we found which fits these data with a good
accuracy includes 8 different subtypes of immune cells and has 15 constant parameters. It postulates
that effector and dividing cells, after depletion of helper cells, become "transient effector" cells
which can differentiate into either anergic or memory cells or die. The model specifies how these
processes are controlled by the antigen and innate (non-CD4) helper cells. The viruses which can infect
helper cells and get them killed by CTL can persist. Conclusion. Testing mathematical model against
several time-dependent parameters measured for similar systems provides us with much more information
about the immune response than both empiric methods and traditional heurisitic modeling are capable of.
Most small modifications of our model lower the quality of fitting dramatically. This indicates that the
model is very specific and worthy of additional experimental tests proposed in our work. 

 Hector Sabelli - University of Illinois at Chicago.

Bios: Mathematical, Cardiac, Economic and Meteorological Creative Processes Beyond Chaos. H. Sabelli, L.
Kauffman, M. Patel, and A. Sugerman

 Bios is a newly found form of organization that resembles chaos in its aperiodic pattern and its
extreme sensitivity to initial conditions, but has additional properties (diversification, novelty,
nonrandom complexity, episodic patterning, 1/f power spectrum) found in natural creative processes, and
absent in chaos. New methods have been developed to measure the properties that differentiate bios from
simpler chaos. Global diversification is the increase in variance with duration of the time series;
local diversification is the increase in variance with increase in embedding [Sabelli and Abouzeid,
Nonlinear Dynamics, in press]. Diversification differentiates three types of processes: (a) mechanical
processes and random series conserve variance; (b) processes that converge to equilibrium or periodic or
chaotic attractors initially decrease variance; (c) creative processes increase variance. Novelty is
defined as the increase in recurrence isometry with shuffling of the data [Sabelli, Nonlinear Dynamics,
2001]. Heartbeat intervals, most economic series, meteorological data, colored noise, and mathematical
bios display novelty. Periodic series are recurrent (higher isometry than their shuffled copy). Chaotic
attractors are neither novel nor recurrent; they have the same number of isometries as their shuffled
copies. Nonrandom complexity is characterized by the production of multiple episodic patterns
("complexes"), as contrasted to the uniformity of periodic, chaotic and random trajectories; arrangement
(the ratio of consecutive to total recurrence) measures nonrandom complexity [Sabelli, J. Applied
Systems Studies, in press]. The process equation At+1 = At + k * t * sin(At) generates convergence to (,
a cascade of bifurcations (including a "unifurcation"), chaos (with prominent period 4), bios and
infinitation, as the value of the feedback gain k * t increases [Kauffman and Sabelli, Cybernetics and
Systems, 1998; Sabelli and Kauffman, Cybernetics and Systems, 1999]. When t is given a negative sign,
biotic series show irreversibility while chaotic series show only hysteresis. Bios is composed of
multiple chaotic complexes, and change their range and sequence with minor changes in initial conditions
(global sensitivity); chaotic trajectories are bounded within one basin of attraction (local sensitivity
to initial conditions). The generation of bios requires bipolar feedback. When the bipolar feedback
provided by the trigonometric function is biased, the equation produces a time series that culminates in
chaos with prominent period 3 similar to that observed with the logistic equation. Conservation is
required for bios, which is replaced by chaos in At+1 = k * t * sin(At). The series At+1 - At of a
biotic time series is chaotic; differentiating an empirical series prior to analysis may transform a
biotic pattern into a chaotic one. Mathematical bios and heartbeats show 1/fN power spectra; the time
series of differences shows a direct relation between frequency and power. Empirical and mathematical
biotic series show asymmetry, positive autocorrelation, and extended partial autocorrelation. Random,
chaotic and stochastic series show symmetric statistical distributions, and no partial autocorrelation.
(Supported by SACP). 

 Fabrice Saffre - BTexact Technologies

RAn (Robustness Analyser)

 Robustness of complex networks has been extensively discussed in the scientific literature for the last
few years. Several authors have pointed out that different topologies would react differently to node
failure and/or broken links (see e.g. Albert et al., 2000; Cohen et al., 2000) and that mathematical
techniques used in statistical physics could effectively be used to describe their behaviour (see e.g.
Callaway et al., 2000). It has also been demonstrated that most artificial networks, including the
Internet and the World Wide Web, can be described as complex systems, often featuring "scale-free"
properties (see e.g. Albert et al., 1999; Faloutsos et al., 1999; Tadic, 2001). In this context, it is
becoming increasingly obvious that the robustness of a wide variety of real distributed architectures
(telecommunication and transportation networks, power grids etc.) is essentially a function of their
topology, and could therefore be evaluated on the basis of their blueprint. Similarly, several
alternative designs could be compared before their actual implementation, in order, for example, to
balance redundancy costs against increased resilience. RAn is the software embodiment of a mathematical
framework developed to quantify complex networks' behaviour when submitted to cumulative node failure.
It is designed to test the robustness of any given network topology in an automated fashion, computing
the values for a set of global variables after performing a statistical analysis of simulation results.
Those variables, characterising the decay of the network's largest component, effectively summarise the
system's resilience to this form of stress. 

 Hiroki Sayama - NECSI

Spontaneous Formation of Isolated Groups and its Effects on Genetic Invasion in Locally Mating and
Competing Populations. Hiroki Sayama, Marcus A. M. de Aguiar, Yaneer Bar-Yam, and Michel Baranger

 We present a theoretical model of evolution of spatially distributed populations in which organisms
mate with and compete against each other only locally. We show using both analysis and numerical
simulation that the typical dynamics of population density variation is spontaneous formation of
isolated groups due to competition for resources within neighborhoods that are local but range over
several spatial sites. Such population gaps strongly affect the process of genetic invasion by local
reproductive mixing, and even non-homogeneous genetic distribution is possible as a final state. We then
particularly consider a specific version of this model in the presence of disruptive selection, favoring
two fittest types against their intermediates. This case can be simplified to a system that involves
just two non-conserved order parameters: population density and type difference. Since the co-existence
of two fittest types is unstable in this case, symmetry breaking and coarsening occur in type
difference, implying eventual dominance by one type over another for finite populations. However, such
coarsening behavior may be pinned by the spontaneously generated population gaps between isolated
groups. The long-term evolution of genetic composition is sensitive to the ratio of the mating range and
the competition range among other parameters. Our model may provide a theoretical framework for
consideration of various properties of spatially extended evolutionary processes, including spontaneous
formation of subpopulations and lateral invasion of different types. 


Jeffrey C. Schank - University of California, Davis

Cycle Variability, Follicle Competition, and Female Mate Choice

 It has long been thought that at least some female mammals (including women and Norway rats)
synchronize their ovarian cycles when in close proximity. However, re-analyses of these studies has
revealed serious and systematic methodological errors. In retrospect, this is exactly what should have
been expected given that female mammals exhibit considerable variability in the lengths of their ovarian
cycles and cycle variability prevents synchrony. Moreover, despite the ubiquity of cycle variability in
cycling mammals, no attention has been paid to possible adaptive functions of cycling and cycle
variability. I present a mathematical model of follicular competition on the ovary that displays (i)
species typical cycle variability distributions, (ii) species typical ovulation rates, and (iii)
compensatory ovulation for biologically realistic parameter values. There are also plausible parameter
values that produce a high degree of cycle regularity. But, apart from artificial selection on some
domestic mammals, wild mammals, in general, do not have highly regular cycles. I then argue that cyclic
ovulation together with cycle variability in some mating systems may enhance a female's ability to
choose phenotypically high quality mates while minimizing competition for quality mates with other
females. 

 Erich Schmidt - Princeton University

Evaluating Affinity Maturation on NK Landscapes. Erich Schmidt Steven Kleinstein

 Individual lymphocytes make response decisions based on signals received from their environment. These
cells coordinate with other cells of the immune system to produce an effective response. We present a
framework to study this decision making process along with an example of its application. We use a
simple neural network to represent the internal decision making process of a cell. Signals from cell
surface receptors are translated into probabilities of actions such as division or apoptosis. The
network is evaluated by determining how well the cell performs in a simulated environment. We then
search the parameter space to determine the ‘best’ cell program, within a set of biological
constraints. As an example, this approach is applied to study the process of affinity maturation in
germinal centers. Specifically, we evaluate the ability of a clonal opulation of B cells to efficiently
find the highest affinity points on a fitness landscape. In this case, the environmental signal is the B
cell receptor affinity for antigen, and one decision the B cell can make is whether or not to mutate its
receptor. We use an NK model for the landscape, with parameters adjusted to produce a realistic affinity
landscape. 

 Pierre Sener

 Frustrated chaos is a dynamical regime which appears in a network when the global structure is such
that local connectivity patterns responsible for stable oscillatory behaviours are intertwined, leading
to mutually competing attractors and unpredictable itinerancy among brief appearance of these
attractors. In this paper, through a detailed study of the bifurcation diagram given for some connection
weights, we will show that this frustrated chaos belongs to the family of intermittency type of chaos.
The transition to chaos is a critical one, and all along the bifurcation diagram, in any chaotic window,
the duration of the intermittent cycles, between two chaotic bursts, grows as an invert ratio of the
connection weight. We will more specifically show that anywhere in the bifurcation diagram, a chaotic
window always lies between two oscillatory regimes, and that the resulting chaos is a merging of, among
others, the cycles at both ends. Since in our study, the bifurcation diagram concerns the same
connection weights responsible for the learning mechanism of the Hopfield network, we will discuss the
relations existing between bifurcation, learning and control of chaos. We will show that, in some cases,
the addition of a slower Hebbian learning mechanism onto the Hopfield networks makes the resulting
global dynamics to drive the network into a stable oscillatory regime, through a succession of
intermittent and quasiperiodic regimes. 

 Benjamin Shargel - NECSI /
Icosystem Corporation

Optimization of Robustness and Connectivity in Complex Networks

 It has recently been shown that many complex biological, social and engineered systems can be modeled
as inhomogeneous networks, whose connectivity distributions follow a power-law, implying no
characteristic scale. Such scale-free networks possess a great degree of interconnectedness due to the
efficient use of a relatively few number of highly connected nodes. Scale-free networks are often
contrasted with exponential, or purely random networks that are less interconnected but retain a greater
robustness to attack. In this paper we present a general framework of complex networks, of which both of
these network types are special cases, by parameterizing two aspects of network construction: growth and
preferential attachment. We find that the growth and preferential attachment parameters tune the attack
tolerance and interconnectedness of the network in a largely independent manner, enabling the
construction of a new type of network that has the interconnectedness and failure tolerance of a
scale-free network but a robustness to attack closer to that of an exponential network. 

 Christopher A. Shaw - Univ. British Columbia

Reverse Engineering Neurological Diseases

 The causes of the 'age-related' neurological disorders (Alzheimer's disease, Parkinson's disease, and
amyotrophic lateral sclerosis (ALS)) remain unknown. These disorders cannot be currently detected until
irreparable damage has been done to the nervous system. Largely for this reason, these disorders are
incurable and likely to remain so for a variety of theoretical reasons. Without knowing causal
mechanisms, no targeted pharmacological strategy can be employed. The costs associated with the
palliative care for victims is enormous. Current hypotheses tend to focus on either genetic
abnormalities or environmnetal neurotoxins as causal. As most cases of such disorders are 'sporadic'
rather than 'familial', a purely genetic origin seems unlikely. Environmental factors seem much more
likely to play a dominant role, albeit possibly acted upon by genetic 'propensities'. This conclusion,
while helpful, still leaves a vast number of potential environmental factors to consider, many of which
alone, or in synergy, could cause neuropathological outcomes mimicking the various human disorders. We
have approached the problem using a model system designed to mimic conditions of an unusual neurological
disorder that combines many of the features of Alzheimer's, Parkinson's, and ALS. This disorder is
termed ALS-parkinsonism dementia complex and was once a major cause of death on Guam. Epidemiological
evidence linked the disorder to consumption of the seed of the cycad palm: incidence of the disorder
reached almost 25% of the adult population when cycad seed consumption was high; 'Americanization' of
the Guamanian diet led to a dramatic decline in ALS-PDC. Our animal model involves mice fed cycad flour
similar to that consumed on Guam. The mice are subjected to a battery of behavioral tests for motor,
olfactory, and cognitive function and are sacrificed at various stages after the onset of cycad
exposure. Behaviorally, cycad-fed mice show significant declines in motor abilities followed by losses
in learning and memory. Olfactory function is disturbed. Histological analysis of the central nervous
systems of these animals reveal widespead neurodegeneration associated with areas controlling motor,
olfactory, and cognitive functions. Biochemical analyses show significant changes in various second
messenger pathways (e.g., protein kinases), in ionotropic receptor numbers and distributions, decreases
in the level of glutamate transporter proteins, and abnormal expression of various cytosolic proteins
such as heat shock and tau. Our current goal is to put these changes into sequence with a view to
establishing a time line of pathological events from first exposure to cycad neurotoxins all the way to
cell death and behavioral loss of function. Given the extremely large numbers of molecules that could be
involved in cell death 'cascades', we are attempting to 'reverse engineer' the various stages in our
model. We believe that the likely neurotoxin begins to impact neuronal health by causing excessive
glutamate release leading to excitotoxic actions. Subsequent events include loss of glutmate
transporters and hyperactivation of regulatory protein kinases. 'Downstream' events seem to include tau
protein abnormalities. Experiments now in progress seek to fill in details leading to each of these
stages by selectively examining molecules that are part of specific biochemical pathways. The ultimate
goal remains that of providing a complete sequence of events leading to neuronal cell death. 

 Bob Sheldon - Emergent-IT, Inc.

Comparing the Results of a Non-Linear Agent-Based Model to Lanchester'sLinearModel:Intuitive Guidelines
for Analyzing SOCRATES Output

 The basic mathematical models used by the military to analyze combat operations have changed little in
the last one hundred years, despite the fact that military technology has changed dramatically in the
Information Age. "Project Albert" is a research and development effort by the Marine Corps Combat
Development Command to assess the general applicability of the study of complex systems research to
warfare and to provide candidate models for the study of Information Age military operations. This
presentation discusses a comparison of a Project Albert research effort, the Simulation of Cooperative
Realistic Autonomous Transparent Entities (SOCRATES), with a traditional mathematical combat model 

 Flávio Mesquita da Silva - Compahnia Energetica de Brasilia

Positive Feedback Loops and tracking Principles Applied to Sustainable Community Building: A Case Study

Premise One of the patterns observed in the tracking tradition is that all animals, but human beings
love visiting the 'transition zone', which is a place where environments meet (i.e. prairie and forest,
water and sand, etc.) In other words, diversity is an integral part of nature, and as such animals
naturally accept it. As natural beings, humans can be affected in ways that they may re-discover (or
uncover) how to operate in a diverse social environment. Context The experience took place within the
sole distributor of electric power in the cosmopolitan area of Brasilia, Brazil, the Companhia
Energetica de Brasilia - CEB. Most employees were busy trying to understand the new organizational
architecture, which had been previously designed by a group of consultants, and were uncertain about
what would happen to the 'good old niches', held mostly by well-educated male engineers, who were
immersed in a well-defined hierarchical structure. There was a culture of doers with stiff postures, who
had been following specific rules to accomplish specific tasks. The new architecture was
process-oriented while the former was goal-oriented. There were scattered evidences of a culture of
collaboration. Consequently, the system was not conducive to the generation of feedback loops throughout
the company, especially positive ones. Negative feedback happened as a result of a controlling posture
derived from the framework established within the company, and it was restricted to the scope of those
departments that were directly related to each other. On the other hand, the new model yielded
solidarity. It encouraged collaboration, shared responsibility and, thus, it demanded a radical shift on
the power relationship, from top down to bottom up. The major obstacle, however, was that there was no
way to work on a strategic, shared planning without aligning it to the company's new design. The
complexity resided in aligning people who had distinct levels of perception, and were at different
stages of integration of the already on-going process in the company. Method The overcoming of this
challenge required a paradigm shift of major degree. Principles of positive feedback loops and
Permaculture, and the foundation of tracking (people, in this case) were utilized to design practices
that brought people together to deal with the issue of cultural change in this complex environment. The
key insight was to observe people's behaviors, including their avoidances, and map them, as it is done
in the tracking tradition. As a result, two distinct patterns, which expressed, respectively people's
likes and dislikes, preferences and non-preferences were woven together. As the mapping activity
progressed, the level of intangibles decreased. Conversely, the level of certainty increased in terms of
what could be expected from the partakers. The next step was to generate new, creative impulses into the
system, which took in consideration the patterns revealed by the partakers, so that they could act in
new, proactive ways. Their responses to these inputs generated a sequence of new ways of conversations
and actions, which gradually created the following: a) need for nexus; b) sense of purpose; and c)
shared focus. The turning point came with the emergence of a comprehensive program with several
interesting and intertwined projects in social accountability called CEB Solidary and Sustainable. This
program was created to integrate all once scattered actions on social accountability, connecting one
another, as well as creating new projects. The coordinators of this program became the link between all
parts involved - the company, employees, stakeholders, government, society and environment, and the
systematization of these multi-leveled relationships made the building of a sustainable community
possible. 

 Barry G. Silverman - University of Pennsylvania

 I offer a framework for representing and simulating alternative social psychological theories of crowd
behavior and guerilla organizations and for studying their properties in artificial societies. This
framework is based on utility theory, however, each agent in the artificial world contains a cognitive
model with four layers that help it to sense the nearby environment and other agents actions and to
decide its next course of action. Layer one includes physiological stressors modeled as reservoirs
(energy level, noise tolerance, crowd proximity, etc) that cause decision functioning and performance to
increase or degrade as the reservoirs are replenished or depleted. Layer two uses world value ontologies
containing the agent's standards, preferences, and goals to construe the activation and decay of
emotions in reaction to world events. Layer three summarizes the emotional construals into a somatic
marker, or utility score, and uses that to select the best response (maximin) or action in the (Markov)
chain of possible next states for that agent. Layer four runs the agent procedures for manipulating the
world to carry out its intentions. In this fashion macro behavior emerges from the micro-decisions of
(bounded rationality) agents. The near term goal in creating this framework is to support the analysis
of alternative violence mitigation strategies, though a longer term goal is to interactively support
security force training in immersive worlds. In the current prototype implementation, crowd equilibrium
is modeled at a political protest where agents are variously observing and picketing. An agent
provocateur attempts to taunt the security forces, and if these forces use doctrines that cause them to
manage the crowds poorly and react inappropriately to the taunting, a tipping occurs and a violent
rampage emerges where various unemployed males begin opportunistic looting. The result serves as an
initial test of the theories of several well-known crowd modelers, of how guerilla organizations might
be attempting to destabilize events, and of the effectivity (or not) of various peace-keeping practices.
We are further using this as a testbed to study the role of cellular automata (and genetic evolution)
for population reactions to crowd events and for the study of how best to integrate artificial life and
cognitive agents into a larger simulation of a terrorism campaign. SPONSOR: Pentagon, Defense Modeling
and Simulation Office. 

 Pawel Siwak - Poznan University of Technology

Iterons: the Emergent Coherent Structures of IAMs

Iterons of automata [14] are periodic coherent propagating structures-patterns of symbols- that emerge
in cellular nets of automata. In a sense, they are like fractal objects; they owe their existence to
iterated automata maps (IAMs) performed over a string of symbols. The iterons comprise of particles and
filtrons. The particles, or signals, are well known [2, 5] in classical cellular automata (CAs) model
where iterated parallel processing of strings occurs. They spread and carry local results, synchronize
various events, combine information, transform data, and carry out many other actions necessary to
perform a computation or to complete a global pattern formation process in extended dynamical systems.
The filtrons form another class of coherent objects supported by IAMs. They emerge in iterated serial
string processing which is a sort of recursive filtering (IIR filtering) [11]. In many aspects the
filtrons are like solitons known from nonlinear physics; e.g. they pass through one another, demonstrate
elastic collisions, undergo fusion, fission and annihilation, and form breathers as well as other
complex entities. The first observation of filtron type binary objects has been done by Park, Steiglitz
and Thurston [10]. They introduced a special ST-window sequential operator and a parity update function
- the model called parity rule filter CA, and showed that it is capable of supporting coherent periodic
substrings with soliton-like behavior. Now, a number of particular models exist that support filtrons.
These are iterating automata nets [3], filter CAs [1, 7, 9], soliton CAs [6, 17], higher order CAs,
sequentially updated CAs [3], integrable CAs [4], iterated arrays [5], IIR digital filters or filter
automata [11-14], discrete versions of classical soliton equations (KdV, KP, L-V) [4, 16, 19], and fast
rules [1, 9]. Some new models like box-ball systems [16, 19], crystal systems [8] and quantum affine Lie
algebras were introduced quite recently. All these models that support discrete coherent structures,
indicate at deep and relevant connections between the computational processes occurring within the nets
of automata (represented by IAMs), the equations of motion of nonlinear dynamical systems, and the
behavior of discrete complex systems. In the paper we present a unified automaton approach to discrete
coherent structures. The aim is to expose the idea and the generic role of IAMs in supporting coherent
structures. The very beginning of this idea comes form the paper [18]. We start with the particles known
from classical CAs, and show how they are related to some paths on the associated de Bruijn's graphs.
Then, we show the automata nets where the symbols of evolving strings are separated from the symbols of
automata states. In such nets the strings flow throughout automata, thus the iterated serial processing
of strings is performed in a natural way; filtrons can be observed and analyzed. Again, they are
represented by some paths (sequences) of operations. We show the automata that are equivalent to all
known serial string processing models (mentioned earlier), and we present the filtrons supported by
these automata. Also we show how the filtrons can be analyzed in some associated computations [11, 13].


 Bruce Skarin - Worcester Polytechnic Institute

A System Dynamics Approach to Understanding Terrorism

 After the events of September 11th, 2001 it is clear that terrorism is perhaps one of the largest
complex problems facing modern society in how it affects the entire population in some form. The first
step in trying to solve other large social problems such as racism and gender discrimination began with
creating a general public awareness and understanding. Once a problem has been clearly defined within
the public, new policies tend to receive wider support and more effective implementation. Problems such
as terrorism have grown to a complexity that reaches far beyond the limits of individual understanding.
It has been demonstrated that the human mind's ability to comprehend relationships decreases with every
added causal factor. This leads to weaker decision making strategies that either have a diminishing
impact or tend to enlarge the problem. A lack of knowledge and understanding can also create
debilitating fear and uncertainty in the general public. This can lead to many other economic, social,
and political problems. Terrorism is not new to the world, but its progression has reached a point that
indicates that better understanding needs to be developed. With terrorism's global reach, these
strategies must be understandable and acceptable to many different groups with conflicting backgrounds.
We believe that the use of system dynamics can play a significant role in this process. The goal of this
project (to be completed by 4/30/02) is to develop and test an additional tool for improving public
understanding about the dynamics of religious terrorist organizations. This includes building a detailed
and calibrated system dynamics model of religious terrorist group formation. We will specifically
explore the growth of the Al-Qa'ida terrorist network and the trend of declining incidents and
increasing severity. In addition we will sample the general public's understanding of terrorism dynamics
and evaluate the effects of learning from the model. 

 Benjamin Skellett -
University of Queensland

Classical Dynamics of Magnetically Coupled Spins

 This paper investigates the dynamics of a Hamiltonian system comprising of two coupled spins. This
system can be considered as a model for two interacting magnetic molecules coupled via their magnetic
moments using superconducting loops. Such a system has recently been proposed as a realisation of
quantum bits for use in quantum computing. This system is interesting because without the coupling,
motion is totally linear; the spins exhibit uniform precession. The simple nonlinear interaction is
responsible for generating complex dynamics which we aim to understand and explain.The Hamiltonian for
the system can be written in terms of the components of angular momentum yielding a set of six coupled
differential equations. With the total angular momentum of each spin constant, the motion is confined to
the surfaces of two spheres. In this paper we make use of a stereographic projection to transform
coordinates and reduce the dimension of the phase space before proceeding to analyse the stability and
bifurcation of the equilibrium solutions. Equilibrium occurs when there is no movement in the system,
resulting in a fixed spatial configuration where the forces on each spin are in balance. Since the total
energy in the system remains constant, these fixed configurations will occur only for extreme initial
energy values. We discover that the system is robust to weak coupling and identify a critical value of
the coupling parameter, above which the system undergoes a bifurcation. This bifurcation causes the
equilibrium configurations to move away from the poles, resulting in qualitatively different dynamics.
Trajectories remain regular and localised close to the extreme energy configuration, although the system
displays sensitive dependence on initial conditions and chaotic motion in other regions of the phase
space. Investigating the topology of the constant-energy hypersurfaces allows us to understand the
constrained phase space available for trajectories to explore and is a good indicator of whether to
expect a near-integrable or chaotic regime. By taking Poincare sections we are able to sample the motion
while reducing the dimension of the phase space. This allows us to numerically investigate the
integrability of the system. It also allows us to identify regions of chaotic and regular motion, as
well as verify the analytic results. The correspondence between the Poincare sections and the higher
dimensional dynamics is further established by visualising the motion of each spin on the original
spherical surfaces. The complexity of the motion is clearly visible in the angular momentum coordinate
system. 

 V. Anne Smith - Duke University Medical Center

Using Bayesian Networks to Reverse Engineer Simulated Songbird Brains

 Biological systems are inherently complex, consisting of multiple levels of organization and
interactions. To decipher how biological systems work, one needs to integrate information across these
multiple levels. The songbird vocal communication system is ideal for such integration due to a long
history of ethological investigation and a discrete dedicated brain network. We believe integration can
be accomplished by reverse engineering using functional bioinformatic algorithms. One major limitation
of reverse engineering of biological systems is that there are no current high-throughput intervention
methods to test the accuracy of the generated solution. In anticipation of this limitation, I created an
artificial songbird brain system algorithm, called BrainSim, where I make and know all the rules. I
sample data from this simulated system as one would sample data from a real biological system, and plug
the sampled data into reverse engineering algorithms to recover my rules. Such an approach enables
improvement of the algorithms for better recovery as well as guidance for data collection in later
biological experiments. I began by testing Bayesian networks as a method of reverse engineering. I found
that Bayesian networks are capable of recovering with over 50% fidelity the rules of the simulated
system, and thus show promise for being able to integrate the multiple levels of analysis necessary for
understanding a complex system such as the songbird brain. 

 Arnold Smith -
National Research Council (Canada)

Continuous and Discrete Properties in Self-Replicating Systems

 Since von Neumann's seminal investigations in the 1940s, cellular automata have played a central role
in the computational study of self-replication. In this paper we will argue that simulations that are
closer to certain kinds of artificial chemistry, using cellular (or atomic) elements with a combination
of discrete and continuous attributes, which are free to move in a continuous Euclidean space, can offer
significant advantages over discrete cellular automata. We will show examples of populations in which
structures similar to those in comparable cellular automata not only self-replicate, but whose elements
self-organize to create some of those structures in the first place. This latter property is not
characteristic of living systems, but demonstrating it increases the sharpness of the questions we can
ask about such systems. We also argue that the underlying complexity of these partially continuous
systems is less than for analogous cellular automata, in terms of the initial information required to
seed the systems and define their rules. In our experiments, we start with a population of particles
randomly distributed and with random initial velocities inside a two-dimensional container. For these
experiments, the number of types of particle is small --- two to five. Particles move and orient
themselves with respect to a field that can be thought of as electrical. All particles have a net charge
of zero, but most have a dielectric polarization which gives them an orientation and causes them to
locally attract each other, supporting the formation of simple molecular structures. Several mechanisms
for catalyzing replication are investigated, in some cases involving a special transcription agent, and
in other cases spontaneous cell mitosis under specific conditions. These experiments lead to a
discussion of informational content and complexity in each of the regimes. By varying the binding
parameters, we can vary the robustness and stability of the structures that are created, and we will
discuss some initial experiments on structural competition and evolution. In passing, we describe a
mechanism for temporally and spatially adaptive computational resolution which allows a relatively large
population of freely-moving particles and their interactions to be simulated without enormous
computational cost. This is still exploratory work, but it is expected to have application in due course
to nanotechnology, where very simple structures interacting in a Euclidean space will need to be able to
reproduce, and where robustness, resilience, and population dynamics will need to be carefully modeled.


 Doug Smith - University of Denver College of Law

Order (for free) in the Courtroom: Law as a Complex Adaptive System

 The American jurist O.W. Holmes defined law, roughly, as the prediction of how a trial judge would
react to a situation. Yet, in their more honest moments, lawyers concede that law on the books bears a
less- than - perfect relationship to what trial courts do. Still legal commentators uniformly ignore
trial courts in favor of examination of the written decisions of appellate courts, which represent less
that one per cent of all legal decisionmaking. Law is often described as the examplar of hierarchical
control of a social system. Indeed the bulk of legal writing assumes away trial court discretion and
proceeds as if trial courts' decisions are perfectly deteremined by directives and review of courts of
appeal. Those few writers who have tried to apply the insights of chaos theory and complexity science to
law have, for the most part, been solely concerned with legal doctrine in the forms of the written
decisions of appellate court judges and the statutory enactments of legislatures. Past efforts to
systematize the decisions of trial court judges have faltered at the point of locating any source of
patterns or predictability in trial court rulings. This paper locates the source of patterns in law by
treating law as a complex adaptive system. It proposes a simplified model of a legal system comprised of
two lawyers and a single judge with no a priori decisions or statutes to guide her. It posits that this
simplified system would lead inevitably to an kind of ordered state as the judge is constrained to
choose among the arguments made by our two lawyers who will in turn utilize judge's feedback to (re)
formulate thier arguments. Building from this model, and upon the recent works of Kauffmann, Holland,
Simon, Bruner, Maturana and Varela and Giddens, as well as Luhmann, Teubner, Stephen Winter and George
Soros, this paper examines law from a systems perspective. It describes how a coherent legal system
could emerge, without hierarchical controls, based upon shared cognitive structures (especially as
developed in law schools and in the professioal socialization process)economic and institutional
considerations and interrelationships among repeat players and outsiders within courts. Thus the
articles places a premium on the role of attorneys as active agents in constructing and maintaining the
system. This paper argues that the day-to- day workings of court systems better describes law than
examination of statutes or written decisions of appellate courts. In support three sources of evidence
are examined: the difference between judicial decisions in civil and common law countries, the practice
of landlord-tenant laws in states with statutes that are protective of tenants andstates that are not
and the results of decisions in an emerging area of law, atttacks on predatory lending. The theory of
this paper predicts that legal argumentation and decision-making would be more similar in civil and
common law countries than traditional legal scholarship describes, that landlord-tenant law would be
reach results far more simlar than the differences in landlord-tenant statutes would suggest and that
law of predatory lending, which has largely arisen in the past six or seven years, would differ among
jurisdictions despite largely uniform statutes and common law bearing on the practice. The paper
concludes that legal scholars ignore the systems effects of law at their peril, and that a close
examination of the day-to-day workings of local courts describes law at a greater level of generality
than legal doctrine or treatises on law. 

 Micah Sparacio - Temple University

The Role of Information in the Organization of Complex Systems

 In this paper we explore the role that information plays in the organization of mass-energy into
complex systems. Specifically, we focus on the information flow that occurs when low-entropy information
plays a direct role in the reduction of entropy of a physical system. While complexity and
self-organizational theorists tend to focus on the role of energy flow from an energy source to an
energy sink to explain the organization of complex systems, we hope to highlight the often overlooked,
but crucial role that information plays in this process. In order to accomplish this, we will first
present a definition of information that is richer than traditional Shannon-Weaver theory and
incorporates a sense of functionality and what we call "signal agreement". We wll then demonstrate the
importance of this type of information in the organization of complex systems, beginning with Maxwell's
Demon paradox and then applying the principles to genetic algorithms and biologcal systems. We find that
the universe can best be modeled as an information-responsive system, in which complex systems are the
secondary effect of various information pathways. Thus, we propose that the study of complex systems
should emphasize the crucial role of information and develop a method of detailing the origins and flow
of information. 

 J. C. Sprott - University of Wisconsin - Madison

Predator-Prey Dynamics for Rabbits, Trees, and Romance

 The Lotka-Volterra equations represent a simple nonlinear model for the dynamic interation between two
biological species in which one species (the predator) benefits at the expense the other (the prey).
With a change in signs, the same model can apply to two species that compete for resources or that
symbiotically interact. However, the model is not structurally stable, since persistent time-dependent
(oscillatory) solutions occur for only a single value of the parameters. This paper considers
structurally stable variants of the Lotka-Volterra equations with arbitrarily many species solved on a
homogeneous two-dimensional grid with coupling between neighboring cells. Interesting,
biologically-realistic, spatio-temporal patterns are produced. These patterns emerge from random initial
conditions and thus exhibit self-organization. The extent to which the patterns are self-organized
critical (spatial and temporal scale-invariant) and chaotic (positive Lyapunov exponent) will be
examined. The same equations, without the spatial interactions, can be used to model romantic
relationships between individuals. Different romantic styles lead to different dynamics and ultimate
fates. Love affairs involving more than two individuals can lead to chaos. Strange attractors resulting
from such examples will be shown. 

 Iris Stammberger - Tufts University

Contemporary Models of Creative Cognition: an Ethnographic Review of the Impact of Complexity,
Evolutionary and Computational Theories.

 Using an ethnographic methodology, I review a selected sample of models of creative cognition. The
review shows that despite the fact that the literature in creative cognition is as diverse and
fragmented as is the disciplinary background of researchers interested on the phenomenon, contemporary
models tend to rely on concepts and constructs borrowed from complexity, evolutionary and computational
theories. To explore the possible ways in which seemly irreconcilable models could inform each other, I
identify the computational models as top-down models while the evolutionary models are identified as
bottom-up models. This is based on the distinction offered in Dennett's "Darwin Dangerous Idea" (1995)
that computational and evolutionary approaches in cognitive science can be seen as top-down and
bottom-up analysis, respectively, of the same phenomenon. Dennett distinction also includes complexity
theory as offering a map of the patterns identified by top-down and bottom-up analysis; in such a
framework, interpretations of creative cognition offered by computationalism, evolution and complexity
theory could be seen as complementary. Models of creative cognition tend to borrow from only one
explanatory framework - complexity theory, for instance - simply ignoring models nested on a different
explanatory framework, and therefore contributing to generate what seem to be irreconcilable views of
the phenomenon under study. In those cases in which theorists recognize the differential impact of their
chosen theoretical framework, they do not tend to elaborate on the possible cross-fertilization among
frameworks, and therefore fail to identify what constructs create the divergence among views or what
different conceptualizations could lead to a more coherent understanding of creative cognition. Once the
research question is clear, the ethnographic lens helps validate the selected sample, forces systematic
data analysis, reveals how the process of construction of concepts and categories in each model is
impacted by the chosen nesting framework: complexity, computation or evolution, and illuminate ways in
which theorizing could be cohesive. The selected sample is the collection of essays included in the
following edited publications: 1. Dimensions of creativity (Boden, 1996), 2. Handbook of Creativity
Research (Sternberg, 1999), 3. Creative Thought (Ward et al, 1997), 4. Technological Innovation as an
Evolutionary Process (Ziman, 2000), 5. Changing the world (Feldman et al, 1994), 6. Scientific Discovery
(Langley, 1987), 7. The Nature of Insight (1995). In the paper I discuss the criteria that validate the
sample. What emerges from this exercise is a fresh look to models of creative cognition with the
potential of contributing to a unified theory of the phenomenon. 

 Vinod
Subramanian - University of Cincinnati

Intelligent Broadcast in Random Large-Scale Sensor Networks

 With advances in miniaturization, wireless communication, and the theory of self-organizing systems, it
has become possible to consider scenarios where a very large number of networkable sensors are deployed
randomly over an extended environment and organize themselves into a network. Such networks --- which we
term large-scale sensor networks (LSSN's) --- can be useful in many situations, including military
surveillance, environmental monitoring, disaster relief, etc. The idea is that, by deploying a LSSN, an
extended environment can be rendered observable for an external user (e.g., a monitoring station) or for
users within the system (e.g., persons walking around with palm-sized devices). Unlike custom-designed
networks, these randomly deployed networks need no pre-design and configure themselves through a process
of self-organization. The sensor nodes themselves are typically anonymous, and information is addressed
by location or attribute rather than by node ID. This approach provides several advantages, including:
1) Scalability; 2) Robustness; 3) Flexibility; 4) Expandability; and 5) Versatility. Indeed, this
abstraction is implicit in such ideas as smart paint, smart dust, and smart matter. The purpose of our
research is to explore how a system comprising a very large number of randomly distributed nodes can
organize itself to communicate information between designated geographical locations. To keep the system
realistic, we assume that each node has only limited reliability, energy resources, wireless
communication capabilities, and computational capacity. Thus, direct long-range communication between
nodes is not possible, and most messaging involves a large number of ``hops'' between neighboring nodes.
In particular, we are interested in obtaining reliable communication at the system level from simple,
unreliable nodes. Wireless networks that operate without fixed infrastructure are called ad-hoc
networks, and are a very active focus of research by the wireless community. However, most of the work
focuses on networks with tens or hundreds of nodes, where most message paths are only a few hops long.
All data messages in such a system are unicast, i.e., they are between specific pairs of nodes. There
are two major formulations for this. In some message routing algorithms, a path discovery process is
used to first find a route between the source and destination nodes (or locations), and the message is
then sent along this path. This is clearly a top-down approach with limited scalability. Other routing
protocols use next-hop routing, where each node, knowing the destination of an incoming message, only
determines the next node to forward the message to. These protocols scale much better, but at the cost
of maintaining and updating extensive amounts of information about network topology. This can be
expensive in terms of energy, and can often lead to problems if the individual nodes are unreliable,
causing broken links and lost messages. From a complex systems viewpoint, the problem with unicast-based
next-hop methods is that they do not exploit the inherent parallellism of the system to achieve
robustness. This is the issue we consider in our research. Rather than using directed unicast between
nodes, we study the possibilities of broadcast. In the simplest case, this corresponds to flooding,
where every message received by a non-destination node is ``flooded'' to all the node's neighbors. While
this is a simple apprach, it is extremely wasteful of bandwidth and creates a lot of collisions --- the
simultaneous use of the wireless channel by multiple messages, all of which are lost as a consequence.
To overcome the problems of flooding while retaining its inherent parallellism, we explore the method of
intelligent broadcast. In this approach, each node receiving a message decides whether to re-broadcast
it to all its neighbors or to ignore it. Note that the decision does not involve selecting which
neighbor the message is forwarded to, but only whether to forward the message. The latter is a much
simpler decision, and can be made on the basis of the information carried by the message in combination
with that available within the potential forwarding node. This approach leads to a self-organized
communication process where local decisions by the nodes produce global availability of information. In
the paper, we present a well-developed paradigm for random LSSN's, including a model for the nodes and
viable broadcast-based protocols for channel access and network organization. We evaluate the
performance of the network in the case of simple flooding, and then study the effect of a simple
decision heuristic that allows nodes to limit message re-broadcast based on how many hops the message
has already travelled. We show that this heuristic leads to a dramatic improvement in performance,
making the broadcast-based system a viable --- and more robust --- alternative to more complicated
systems under some conditions. We also characterize how network parameters such as size, node density,
messaging rate and node reliability affect the performance of the heuristic. 


John Symons - University of Texas, El Paso

Emergence and Reflexive Downward Causation 

 Richard P. Taylor - University
of Oregon

The Discovery of Fractals in Jackson Pollock's Paintings: Implications for the Visual Sciences. Richard
P. Taylor, Branka Spehar, Colin W. G. Clifford and Ben R. Newell

 It is well known that natural processes and computer mathematics can generate fractals (patterns that
repeat at many magnifications). Our recent fractal analysis of Jackson Pollock's dripped paintings
demonstrate that humans can also generate fractals (1). This discovery has renewed interest in the human
visual system's response to fractals and, in particular, to the complexity generated by their repeating
patterns. This complexity can be quantified using the fractal dimension D. We show that humans display a
'universal' aesthetic preference for a specific range of complexity (D = 1.3 to 1.5), regardless of
whether the fractals are generated by nature's processes, by mathematics, or by Pollock (2). We will
discuss practical applications of this result (2). Having revolutionized art of the Twentieth Century,
Pollock's work may be destined to have an even broader impact in the Twenty-first Century. 1. R.P.
Taylor et al, Nature 399, 1999, 422. 2. R.P. Taylor, Nature, 410, 2001, 18. 


Robert Tinker

Complex Molecular Simulations in Science Education. Robert Tinker, Amy Pallant and Qian Xie

 The growing understanding among scientists of the central role of complex systems and emergent
properties has not been matched by any significant changes in the K-14 math and science curriculum. One
of the problems is that there is no room in the curriculum for new topics; any additions must either
displace other topics or realize efficiencies in teaching required topics. We have developed a piece of
sophisticated molecular simulation software called Molecular Workbench 2D, and are experimenting with
using this tool to teach a wide range of traditional topics in physics, chemistry, and biology that
represent emergent properties including thermal phenomena, physical properties of materials, reactions,
and biochemical processes. Our preliminary results indicate that, using complex simulations, these
topics are easily taught at least as early as the eighth grade and that the focus on macroscopic
properties that emerge from atomic-scale models results in improved learning of important science
topics. 

 Mark R. Tinsley - The University of Montana

Dynamic Instability in Tropospheric Photochemistry: An Excitability Threshold. Mark R. Tinsley and
Richard J. Field

 Dynamic equations describing photoxidation of tropospheric chemical pollutants are nonlinear,containing
complex feedback loops. Such nonlinearity is known to give rise to variousdynamical instabilities
including multiple steady states, oscillation, and even chaos. A relatedtype of instability,
excitability, is demonstrated here using a two-variable (reduced from six) model of methane
photoxidation in which perturbation of a stable but excitable steady statebeyond a threshold is
dramatically amplified before the steady state is reapproached. Suchswitching/amplification responses
may have important implications for atmospheric/climaticmodeling. A phase-plane analysis describes the
origin of this excitability and suggests that itmay be a relatively common phenomenon in environmental
models. 

 Jochen Triesch - UC San Diego

Towards Understanding Self-organized Information Flow in the Cortex

 One of the most fundamental questions in the neuro- and cognitive sciencesis how the flow of
information through the brain is organized(1). For example,the same visual stimulus may have very
different consequences and may leadto very different perceptual and behavioral reactions when occurring
indifferent contexts or behavioral states. The brain must somehow manage toroute the right pieces of
information to the right places at the right time.In this light, the problem of behavior organization
can be seen as essentiallyan information routing problem. It seems unlikelythat there is an "overseer"
brain structure that has global control of whatinformation is passed from where to where but it seems
more likely theinformation flow is self-organized to a large extent. We are studyingsimple networks of
modules which can adapt the information routing betweenmodules on roughly the same time scale as
processing within modules takesplace(2). To this end a functional coupling variable is defined between
modulesthat is subject to fast dynamics resulting in dynamic information routingthrough the network. The
dynamic information routing is driven by a completelydistributed process adapting the functional
coupling between modulesbased only on locally available information and is essentiallyHebbian(3) in
nature. We study simple network models with different module topologies withreciprocal connectivity:
strictly hierarchical, fully connected, and mixedparallel/hierarchical topologies as found in the
primate cortex.We analyze the resulting dynamics using simulations. As a result of the
dynamicself-organized information routing, networks can quickly form cliques ofstrongly interacting
modules that are relatively isolated from other cliques.These emergent network dynamics bear an
interesting resemblance to a numberof phenomena in human and animal perception including sensory
integration andsegmentation, attention, rivalry and ambiguity, automaticity ofprocessing, and dual task
performance. From the viewpoint of dynamicinformation routing these issues bear interesting
similarities, suggestingthat similar neural mechanisms might play a role.We discuss candidate mechanisms
for the implementation ofthe dynamic self-organized information routing in the brain and theirbiological
plausibility. (1) E Salinas and TJ Sejnowski (2001), Nature Reviews Neuroscience, vol 2,p 539-550.(2) C
vd Malsburg (1981), "The correlation theory of brain function".Internal Report 81-2, Max Planck
Institute for Biophyscial Chemistry,Goettingen, Germany.(3) DO Hebb (1949), "The Organization of
Behavior", New York: Wiley. 

 John Trimble - Howard University

Coping with Complexity in Knowledge Management

 Knowledge management systems have experienced increased complexity due largely to the exponential
growth in information technology. The increasedavailability of information and increased communication
among stakeholders are central aspects of the formation of complex knowledge managementsystems. These
systems are multifaceted and consist of both implicit and explicit components. Coping with this
increased complexity requires effectively dealing with the knowledge acquisition process and the
knowledge representationapproaches necessary in transforming individual mental models into computational
models and collective conceptual models that form the basis of aneffective knowledge management system.
This project builds on knowledge acquisition techniques and approaches developed in the study of expert
systems, systems management and cognitivescience. In particular, it draws on previous work by the
authors involving knowledge acquisition in system dynamics, intelligent tutoring systemsand software
development projects. Knowledge elicitation, and group techniques are the primary focus since they
facilitate the progression fromindividual mental models to collective conceptual and computer based
models. The identification of implicit knowledge management components andimplicit knowledge is the most
serious challenge to knowledge acquisition processes. Knowledge representations render complex knowledge
repository problems manageable by the appropriate stakeholders. An ontology is the naturalextension of a
knowledge representation . It allows a more broadly shared formal conceptualization of a particular
domain. Ontologies allow bothcomputer agents to navigate knowledge repositories and humans to confront
knowledge management system complexity. A major issue concerningontology development is ³how broad of a
domain should the ontology address?². Different ontological paradigms are examined. They are approached
astwo broad categories : 1) compositional and static relationships and 2) dynamic and causal
relationships. These two broad categories mirror thestructural and temporal complexity of knowledge
management systems. Structural paradigms examined include object oriented languages, semanticnetworks
and frames. Dynamic system paradigms include event-based dynamics, process-based dynamics, dialectical
change and system dynamics. The general knowledge management system under study is the University
academic system. The particular system under study is academic knowledge atHoward University. This
effort is part of a larger project that involves the study of intelligent knowledge engineering and
management . Thispaper will report on the application of select knowledge acquisition techniques, within
the Systems and Computer science Department at HowardUniversity, to determine the best ontological
approaches and artifacts to navigate the complexity of the department¹s knowledge management system. 

 Len Troncale - Cal Poly University

Stealth Studies in Complex Systems that Completes Science GE Requirements at Most Universities

 Systems science research, and its application to specific domains such as systems biology, is expanding
very rapidly. While basicresearch activity occurs at graduate-level centers at major universities, there
are increasing commercial applications. The future health of bothendeavors depends, in part, on the
existence of a healthy pipeline that feeds and directs pre-college and undergraduate studentsinto these
new, unconventional fields. Yet many of the systems education programs of the past three decades failed
to survive, even as newprograms were being initiated. What can we learn from the early failures and how
can we push awareness of complex systems to earlier grades toensure a healthy feeder pipeline? This
presentation will contrast successful and unsuccessful past programs in a search for guidelines, and
willcall for the NSF to support comprehensive studies or conferences specifically on the health of and
progress in systems education. The presentation will describe a NSF-supported program that will soon be
ready for adaptation to other campuses as an extended example. This newprogram essentially is
stealthsystems science education for every university. It would allow undergraduates from any major
tofulfill the universal science general education requirement by taking a yearlong course centered on
systems mechanisms. These processes aresimilar in many natural science phenomena, so are used as
Integrative Themes to synthesize and simplify the knowledge base of seven sciences(astronomy, physics,
chemistry, geology, computer science, and mathematics). Students are first taught how to recognize a
specific systems process(like cycling, duality, hierarchy, chaos, feedback, etc.) in new material, and
then are sequentially presented with dozens of case studies in thephysical, biological, and symbolic
sciences that illustrate how the process is common to all of these sciences. This approach gives
students acommon, repeating framework for the many new terms and relationships for each case study.
Students learn considerably greateramounts of science, and learn systems science simultaneously. The
exact same processes are then shown in social and technology systems, even inthe arts and humanities.
Students leave the course with a feeling of the unity of science, and the utility of science and systems
science in theirdaily lives. Course methods are as integrated and interdisciplinary as the
pre-integrated material. They use the advantages of a hybrid approachbalancing the technological with
the human. Students attend weekly discussion sessions, skill-training sessions, and team-
completedinterdisciplinary labs, on the same topics as the multimedia modules. The computer-based
courseware has 15 special multimedia aids to learningembedded within it, as well as a dozen special
learning aids. The result, shown in nine test offerings of the course on two CSU campuses, isincreased
efficacy of learning even for science phobic students. Data will be presented indicating how much the
students learn, howwell they learn it, and how they judge the hybrid e-learning/collaborative learning
methodology compared to conventional lectures.Perhaps the most important aspect of this example is its
potential for widespread dissemination as a Systems Integrated Science text, and as aGeneral Education
alternative for many of our 2,500 universities, including training teachers for a multiplier effect on
K-12. Such stealthstudies in systems science could contribute significantly to the long-term health of
the complex systems worker pipeline.

An Open Source Computer-Based Tool for Research and Education in the Systems Sciences

 XML-Java has been used effectively by several different communities of scholars to improve their
communication, speed introduction of new workersto the field, and as a mechanism to spread the use of
standards, or its opposite, the juxtaposition of alternative schools ofthought to enable faster
development. A well-developed example of XML serving these functions is the building and use of CHML for
the fieldof chemistry. This special interest workshop will call together anyone at ICCS02 interested in
initiating an;open-source programming effort using XML-Java to build a similar tool for the systems
sciences or sciences of complexity. We would call this new commons database SYSML, an enhancement tool
about systems science and systems scientists, built by systems scientists, for systemsscience
advancement. The field of systems studies has a fairly long, but unusually disconnected history.
Personalities are overtly and overly dominant in such periodsof early development, sometimes to the
detriment of the field. Many promising new avenues of approach have appeared, many new conference
series,new book series, new journals, and many new application domains. But these tend to compete rather
than complement and inform each other. The mostdominant feature of such historical and recent
developments is incredible diversity. Strategies of approach range from basic theoretical research,to
work on methodological tools, to work in the natural sciences, to applications in every conceivable
domain of the social sciences, to evenvirtual worlds. What holds such a diverse field together? How can
these domains be linked to and inform each other? SYSML would be one approach tohelp different schools
of thought to learn about and integrate with each other, rather than compete and promote unintentional
fragmentation of thefield. Graphics developed in the NSF-ISGE project will be used to provide a metaphor
of the integration that is needed. SYSML would focus onincorporating standards for and hotlinks to
presentations of very comprehensive, and interconnected lists of topics such as: Types of
Systems,Alternative Taxonomies of Systems, Systems Research Workers, Systems Research Institutions,
Systems Journals, Systems Book Series, Key FutureQuestions, Systems Conference Series, Consensus Systems
Mechanisms/Processes, their Identifying Features, Identifying Functions, Discinyms,Transdisciplinary
Testing and Verification, Examples and Exemplars, and any others this self-appointed team of SYSML
builders can brainstorm intoexistence. We would expect thorough debate and discussion to occur during
this planning session on whether such a tool would be useful or damagingto the development of the field,
and how it can be kept as neutral as possible, and as useful as possible for both experienced workers
and novicessimultaneously. 

 Ing-Jyh Tsang - Alcatel Bell N.V.

Diversity, Cluster Entropy and Complexity on Randomly Occupied Lattices. I.R. Tsang and I.J. Tsang

 Diversity is an important characteristic of nature and have been used to describe the complexity of
different systems. The term ``complexity'' hasbeen associated with the diversity in the length scales
that the clusterscan assume in the randomly occupied lattices model [1]. Moreover, diversitycan refer to
different properties of the system, such as the size orconfiguration assumed by the clusters. Thus,
``cluster diversity'' isdefined as the differentiation of clusters in respect to their size orLattice
Animals (LA). Entropy is a fundamental concept in physics. It is related with theinformation content and
order/disorder of a system. Here, we consider``cluster entropy'', which is defined using the probability
that an occupiedsite belongs to a cluster of size s, or being part of a specific free orfixed LA [2].
This definition can be associated to the information entropyand the configurational or local porosity
entropy defined in [3]. However,these entropies are based on the information content of a sliding m X
msquare region, thus being basically a local or short-range measurement.Conversely, our definition of
entropy depends on the structure of theclusters, which are not limited to a local region and are capable
ofspanning over the whole lattice. We analyse the cluster diversity and cluster entropy of the system,
whichleads to the determination of probabilities associated with the maximum ofthese functions. We show
that these critical probabilities are associatedwith the percolation transition and to the complexity of
the system. [1] I. R. Tsang and I. J. Tsang, Phys. Rev. E 60, 2684 (1999). I. R. Tsang and I. J. Tsang,
J. Phys. A: Math. Gen. 30, L239(1997). I.J. Tsang and I.R. Tsang. Unifying Themes in Complex Systems:
Proc. of the First NESCI Inter. Conf. on Complex Systems edited by Yaneer Bar-Yam (Perseus Book,
Reading, MA, 1999) [2] I.J. Tsang, I.R. Tsang and D. Van Dyck, Phys. Rev. E 62, 6004 (2000). [3] C. D.
Van Siclen,Phys. Rev. E 56, 5211 (1997); F. Borger, J. Feder, T. Jossang and R. Hilfer, Physica A 187,
55 (1992); C. Andraud, A. Beghdadi and J. Lafait, ibid. 207, 208 (1994); C. Andraud, A. Beghdadi, E.
Haslund, R. Hilfer, J. Lafait and B. Virgin ibid. 235, 307 (1997). 

 Jiri
Vanicek - Harvard University

Replacement Manifolds: A Method to Uniformize Semiclassical Wavefunctions. Jiri Vanicek and Eric J.
Heller

 We present a new semiclassical technique which relies on replacingcomplicated classical manifold
structure with simpler manifolds, which arethen evaluated by the usual semiclassical rules. Under
circumstances wherethe original manifold structure gives poor or useless resultssemiclassically the
replacement manifolds can yield remarkable accuracy. We show how the method can be used to uniformize
cusp singularities in amodel of a 2D electron flow if complex replacement manifolds are takeninto
account. 

 Gregory J. Velicer - Max-Planck Institute for Developmental
Biology

Altruism and Social Conflict in the Bacterium Myxococcus Xanthus

 The myxobacteria are a group of primarily soil-dwelling bacteria that exhibit social migration,
multicellular development of fruiting structures, and social predation on other microbes. Upon
starvation, local populations of Myxococcus xanthus aggregate and initiate a cascade of intercellular
communication that guides fruiting body development and sporulation. During this social developmental
process, a minority transforms into stress-resistant spores, while a majority of each aggregated
population appears to perish, and thus appears to exhibit a dramatic instance of altruism at the level
of individual microorganisms. Some laboratory-generated genotypes that are defective at development in
isolation are able to cheat on a developmentally-proficient wild-type in mixed populations during
development, thus showing exploitation of a complex cooperative system at the genetic, as well as
individual, level. When rare, these cheaters produce spores more efficiently than the wild-type during
development of mixed populations, despite being much poorer than the wild-type during development in
separate, isogenic populations. When allowed to compete with the wild-type over several cycles of
development and growth, distinct cheater genotypes show a variety of competitive fates and effects on
total population dynamics. The ease of generating cheater genotypes in the laboratory (via both
experimental evolution and genetic manipulation) raises questions about the prevalence of cheating in
natural populations and the selective forces, population structures, and potential policing mechanisms
that act to maintain altruistic genotypes in the wild. 

 Burton Voorhees -
Athabasca University

Virtual Stability as a Conceptual Principle Underlying the Directionality of Evolutionary Processes

 A system will be said to be virtually stable when it is employing self-monitoring in order to maintain
itself in a state that would otherwise be unstable. Since such operation requires energy expenditure,
there must be a corresponding benefit that justifies this expenditure. This benefit is greater
flexibility in response to environmental perturbations. At a minimum, the capacity for vitural
stabilityrequires that a system be able to monitor its states with a frequency great enough that only
small corrections are necessary to maintain itself in the virtually stable state. In essence, an ongoing
small energy expenditure is used in order to avoid the need for occasional major energy expenditures,
and an ongoing high frequency of self-monitoring purchases the capacity for more rapid episodic
reactions to environmental changes. This provides an evolutionary advantage favoring organisms with
sensory and motor systems capable of maintaining virtually stable states. In this paper we providea
number of examples of various aspects of vitural stability, and based on these carry out an analysis of
the concept as it relates to other general evolutionary principles, including requsite variety,
edge-of-chaos, and the distinction made by Crutchfield and his collaborators between fittness barriers
and entropy barriers. 

 Michael J. Wade - Indiana University

Gene Interactions

 The genetic architecture of a phenotype consists of the genes, the interactions among them (epistasis),
and the interactions among genes and environments (G x E) that affect the phenotype's expression. For a
phenotype with a 'complex' genetic architecture, epistasis and G x E play significant roles as opposed
to a phenotype with a 'simple' architecture, in which interactions of any sort are relatively
unimportant. Epistasis contributes to inbreeding depression, developmental homeostasis, plasticity,
evolution of sex and recombination, mating system evolution, speciation, and interdemic selection,
because all of these topics involve phenotypes with a complex genetic architecture. For example, in
speciation, epistasis contributes to reproductive isolation because genes that function well in the
genetic background of conspecifics function poorly in the genetic background of inter-specific hybrids,
decreasing their fertility and viability. Such a change in the sign of a gene's effect from positive to
negative can only be caused by interactions with other genes or with the environment. In metapopulations
with migration, interactions function like constraints in that they limit the rate of adaptive
evolution. When migration is halted, however, the constraint is removed and the rate of adaptive
evolution is accelerated. Thus, interactions are extremely important to the origin of biodiversity. 

 Keith Warren - Ohio State University

The Sum of the Parts: Two Studies of Interpersonal Influence on Aggressive Behaviors in Small Group
Settings. Keith Warren, Elena Irwin, Brian Roe, William Sean Newsome

 Economists and other social scientists have recently shown considerable interest in interactions-based
models that address the question of how often and when an individual's choices depend on those of peers
(Brock & Durlauf, 2000; Manski, 1995). Most of these studies have focused on neighborhoods and schools
as the units of analysis, in an attempt to understand interactions among large numbers of individuals.
However, most social interventions occur in classrooms, small groups or dyads, and there is a need to
model interactions in these smaller-scale settings. This paper describes studies that attempt to model
the effect of interpersonal interactions on aggressive behavior in two different settings, the first a
sample of elementary school classrooms and the second a group home for developmentally disabled adults.
In each case we argue that the value of aggressive behavior will depend on whether others are behaving
aggressively; aggression will, therefore, tend to breed aggression. In the first study, we employ a
logistic regression model and find that the average level of aggression in elementary school classrooms,
measured by the teacher, is positively correlated with clinically significant levels of aggression in
individual children, measured by their parents, both concurrently and at one and two year follow-up.
This correlation remains statistically significant when controlling for family and neighborhood poverty,
family conflict, gender, academic achievement and level of aggression in the previous time period. This
suggests that early classroom-based interventions might have a lasting effect on aggressive behaviors in
children. In the second study, we use multivariate nonlinear time series analysis to study the
interactions of two individuals, one male and one female, living together in a group home. We find
evidence of correlated volatility in their aggressive behaviors. In this case, the volatility of the
female resident's behavior is negatively correlated with that of the male resident on the next time lag.
The volatility of the male resident's behavior is positively correlated with that of the female
resident. This mix of positive and negative correlation implies that staff interventions can have
unexpected iatrogenic effects at times of resident behavioral volatility. This presentation has
methodological as well as substantive implications. It emphasizes the critical importance of choosing
the correct unit of analysis, whether it is neighborhood, classroom, or peer group, if we wish to find
evidence of interpersonal interactions. Further, some types of interactions, such as correlated
volatility between individuals, may only be possible to document in a nonlinear time series framework.
Brock, W. & Durlauf, S. (2000). Interactions-based models. In Heckman, J. & Leamer, E. (eds) Handbook of
Econometrics, 5th Edition. Amsterdam: North Holland. Manski, C. (1995). Identification Problems in the
Social Sciences. Cambridge, MA: Harvard University Press. 

 Richard A. Watson
- Brandeis University

Compositional Evolution: Evolvability, Modularity, and Symbiosis

 Certain kinds of complex systems, considered unevolvable under normal accretive change, are, in
principle and under certain circumstances, easily evolvable under compositional change. We use the term
'compositional' to refer to mechanisms that combine together systems or subsystems of genetic material
that have been semi-independently pre-adapted in different lineages. Examplesinclude: sexual
recombination (in subdivided populations), naturalhybridization, horizontal gene transfer, and
endosymbiosis. In contrast, we use the term 'accretive' to refer to variation mechanisms that accumulate
random variations in genetic material, (i.e. the new genetic material introduced by such changes has not
been pre-adapted elsewhere as a set). Thus accretive evolution is driven predominantly by 'successive
slight modifications', and underlies our common understanding of evolutionary change and evolutionary
difficulty. Examples include: genetic mutation, and sexual recombination (inpanmictic populations).We
provide two highly abstract computational models to illustrate a sufficient set of mechanisms and
conditions for compositional change. The first is based on sexual recombination; the second is based on
hierarchical encapsulation of symbiotic groups inspired by serial endosymbiosis and the major
evolutionary transitions. We discuss the likelihood of evolving particular kinds of complex adaptations
under compositional and accretive mechanisms. In particular, we define a class of adaptive landscape,
arising from a highly epistatic butmodular substructure, which typifies characteristics that are
difficult for accretive evolution yet easy for compositional mechanisms. Specifically, this landscape is
highly rugged, has many local optima with broad fitness saddles, and it includes complex adaptations
that appear irreducibly complex, and cannot be reached by paths of small changes with monotonically
increasing fitness. Nonetheless, we show that, under particular conditions, complex adaptations of this
kind are easily evolvable under compositional mechanisms. Our results emphasize the importance of
understanding the qualitative structure of an adaptive landscape and that certain mechanisms in some
circumstances cannot be appropriately characterized as merely a different source of accretive change. 

 Thomas J Wheeler - University of Maine

Interdisciplinary Conceptual Model Blending. Thomas J Wheeler and Mary Dolan

 It is becomming common that research and system's development effortsare being undertaken by
multidisciplinary teams, for several reasons. The first is that insights from several points of view
provide a richer understanding of issues and more opportunities for problem solutions. The second is
related; often, an insight in one disciplinecomes from a thought pattern from another discipline. Third,
in many disciplines the research in parts of the domain has reached the stage where exploring issues and
advances in adjoining parts and in the interaction of parts is warrented. Lastly, a special case of this
comes from the hierarchical nature of systems, especially life, wherein research at individual levels is
different in kind from research at others and integration across levels has become possible and
desirable. While this presents a marvelous opportunity, there are serious problems. In multidisciplinary
research, merging of the disciplines' conceptualizations must occur, at least in the (separate)minds of
the collaborators, but also in the resulting or supporting systems. Increasing use of computer databases
for organizing research and its results leads to data integrations problems for multidisciplinary
research/systems. Each database is designed independently, in accordance with a domain's conceptual
model specialized to a particular research effort, then encoded using general purpose data models.
Because of the independence of development, the differing cultures of the fields, and other reasons,
incompatibilities occur at interfaces between models and systems. Because of the general purpose nature
of the data models, domain insight and intuition, which is clear in each domain's natural illustrations
and explanations of its key models, is lost.This paper explores a mechanism and a methodology for
integration of separate discipline's models, based on distilling the inherent structure of each model
and blending them to create the structure for the integrated domain. It delineates a number of
incompatibility dimensions, and addresses integration issues at the terminology, semantics, pragmatic
and activity levels.The approach has four aspects. The first consists of integrating the "natural"
graphic depictions and explanations each discipline makes of its core concepts, with the general purpose
models of their systems. The second extracts the underlying structure of the natural models based on
analysis of the metaphorical underpinnings of those models. The third creates a blend of the models'
structures, using the character of one to underlie the semantics composed from elements of other models
mapped onto slots of the core model. The fourth creates a framework for visualization of the blended
domain by creating natural depictions and explanations of the blended domain, from the blended structure
and its underlying metaphors. This technique provides a framework for understanding, organizing and
supporting interdisciplinary work, while improving the conceptual modeling process by integrating more
domain insight into the modeling process. We will illustrate the mechanisms and the methodology with
cases from interdisciplinary projects in molecular biology and ecology. 

 Elin
Whitney-Smith - George Washington Univ

Extinctions and the Evolution of Ecosystems: Systems Dynamics and the end of the Pleistocene

 At the end of the Pleistocene, there were significant climate changes and, following the appearance of
Homo Sapiens on each major continent, significant megafaunal extinctions. The leading extinction
theories, climate change and overkill, are inadequate. Neither explains why: (1) browsers, mixed feeders
and non-ruminant grazer species suffered most, while ruminant grazers generally survived, (2) many
surviving mammal species were sharply diminished in size; and (3) vegetative environments shifted from
plaid to striped (Guthrie, 1980.) Nor do climate change theories explain why mammoths and other
megaherbivores survived changes of similar magnitude. Although flawed, the simple overkill hypothesis
does link the extinctions and the arrival of H. sapiens. Mosimann & Martin(1975) and Whittington & Dyke(
1984) quantitatively model the impact of H. Sapiens hunting on prey. However, they omit the reciprocal
impact of prey decline on H. Sapiens; standard predator-prey models, which include this effect,
demonstrate that predators cannot hunt their prey to extinction without themselves succumbing to
starvation.I propose the Second-Order Predation Hypothesis , a boom/bust scenario: upon entering the New
World, H. sapiens reduced predator populations, generating a megaherbivore boom, then over-consumption
of trees and grass, and, finally, environmental exhaustion and the extinctions. The systems dynamic
model developed in this work (available from http://quaternary.net/extinct2000/) specifies
interrelationships between high and low quality grass, small and large trees, browsers, mixed feeders,
ruminant grazers and non-ruminant grazers, carnivores, and H. sapiens driven by three inputs: H. sapiens
in-migration, H. sapiens predator kill rates, and H. sapiens food requirements It permits comparison of
the two hypotheses, through the setting of H. sapiens predator kill rates. For low levels of the inputs,
no extinctions occur. For certain reasonable values of the inputs, model behavior consistent with
Second-Order-Predation: carnivore killing generates herbivore overpopulation, then habitat destruction,
and ultimately differential extinction of herbivores. Without predator killing, extinctions occur only
at unreasonable levels of in-migration. Thus, Second-Order-Predation appears to provide a better
explanation. Further, the boom-bust cycles suggest we over-interpret the fossil record when we infer
that the populations decreased steadily, monotonically to extinction. 

 Robert
L. Wiebe - Boeing; Air Traffic Mangement

Questions from Complexity Science to develop new perspectives on Air Traffic Management

 The Air Traffic Management system has manylevels of Complexity within any national airspace, and
becomes even more complex as harmonization of worldwide ATM services is considered for aglobal ATM
system. Questions will be asked such as, can delay be used as a measure of complexity for ATM, is delay
an emergent property of thesystem, are there a few persistent patterns among world airports that can be
used to simplify airspace design, are there sets of minimal rulesthat define behavior (e.g. the manner
in which aircraft are "collected" prior to approach), and can self-organization be a part of an ATM
system. The intent is to present some information that has been collected about the U.S. ATM system,
address these questions with my thoughts, and thenhear from the participants about their thoughts and
answers to those same questions. 

 Janet Wiles - The University of Queensland

Evolving Complex Integrated Behaviour by Masking and Unmasking Selection Pressures. Janet Wiles, James
Watson, Bradley Tonkes and Terrence Deacon

 The evolution of vitamin C dependency in humans has an interestingevolutionary history. Many animals
have the ability to endogenouslysynthesize ascorbic acid (vitamin C). The crucial gene in this synthetic
pathway is for an enzyme (LGO) that catalyses the last stage of synthesis of vitamin C. Anthropoid
primates (monkeys, apes, and humans) don't synthesize their own vitamin C, and the question that arises
is why not, when it seems so useful. It turns out that primates including humans do indeed have the
wasted hulk of such a gene that is no longer expressed and has accumulated irreparable mutational damage
(it is now a "pseudogene" and was identified by Japanese researchers in 1994 using a probe gene from
rat). The loss appears to have happened about 40 Mya, which is the time that some primates became
diurnal, with consequent changes in lifestyle, including a diet including significant amounts of fruit.
A neat just-so-story is that the increased fruit in the animals' diets provided ample regularly
available vitamin C, reducing the selection pressure to maintain the function of the vitamin C producing
gene. Once the gene was corrupted in this lineage, the animals were effectively addicted to fruit, and
trapped in the fruit-eating lifestyle: obligate frugivores. At this point, all the abilities that had
incidentally supported the acquisition of Vitamin C through diet were then placed under a much stronger
selection pressure, causing many to evolve in such a way as to better insure the ubiquitous availability
of this now essential nutrient. In recent work, Deacon hypothesized that this kind of secondarily
distributed selection pressure is a major force in evolution, integrating what were initially a diverse
set of potentially unrelated skills (e.g. colour vision, tooth structure, taste preferences, etc. in
this case) into a metastable multilocus multiphenotype adaptive complex. This distributed selection
pressure acts as a "reverse Baldwin effect" in that abilities specified directly in the genome may
become masked by both internal and external sources, including flexible behavioral abilities, and over
time their genetic specification is lost (a complexity catastrophe in Kauffman's terms). When this
occurs, the individual becomes dependent on the external source or whatever else has provided the
masking effect, and any phenotypic capacities that support this masking (e.g. by providing an externally
redundant nutrient) become increasingly elaborated and integrated through positive selection pressure.
Deacon's terms are "masking" and "unmasking" of selection, to describe these two effects,
respectively--the former related to the Baldwin effect in reverse, the latter to Waddington's "genetic
assimilation"-and "redistribution" of the substrate of adaptation, to describe the result. We extended
Hinton and Nowlan's (1986) simulation of the Baldwin effect to explore how masking and unmasking can
transfer dependence from one gene to many and thereby integrate whole complexes of genes. Hinton and
Nowlan used a chromosome of 20 genes to simulate a needle in a haystack task, such that the individual
received a payoff only when all alleles were set to '1'. The analogy in the above scenario is that
setting all alleles to '1' corresponds to the ability (a0) to endogenously synthesize vitamin C. Their
model was extended by adding k additional gene complexes (a1, a2, ak), each assumed to code for other
abilities based on the coordinated action of 20 genes (totalling 20k genes in the chromosome). The k
other abilities each benefit the individual, but individually, their benefits are significantly less
than that from the first ability. However, together, the k abilities duplicate the function of the first
ability. The time course of the simulations demonstrate the initial advantage of the endogenously
synthesized vitamin C, followed by a transfer of the ability to the complex of genes that mask the
effect. The Baldwin effect has been hypothesized as a potential mechanism for developing
language-specific adaptations like innate Universal grammar or an innate modular "language faculty" or
in fact any other highly modular capacities. These simulations support Deacon's argument that the
process is likely the inverse, and that the extensive neural and other anatomical consequences would not
be in the form of specific innate adaptations that make the acquisition process more innate. Instead,
the power of symbolic communication as a masking agent should unmask selection on an extensive and
highly distributed constellation of capacities that would collectively come under selection for their
fractional contributions to the acquisition and use of language. 

 Kwang Woo Park
- Claremont Graduate University

Income Distribution Dynamics: Marriage and Informational Cascades

 This paper investigates the role of household formation on income distribution dynamics. This is
accomplished by building an age-structured general equilibrium model in which agents are endowed with
physical and psychological attributes that affect marriage and fertility decisions. Personal
characteristics are transmitted from parents to children resulting in intergenerational persistence of
marriage patterns and houseshold income. Further, psychological factors allow fads and fashions to
impact distributional dynamics. After calibrating the model, the dynamics of several variants of the
model are simulated and tested against the data. We find that psychological factors affecting marriage
explain a substantial proportion of income distribution dynamics. 

 Hajime
Yamauchi - U of Edinburgh

Evolution of Language Universals under Baldwinian Niche Construction

 While considerable superficial differences exist among human languages, it has been noticed for long
time that a non-trivial level of commonality exists in human languages. The generative grammar led by
Noam Chomsky has attributed such structural (i.e. grammatical) universals to a special innate
specification of language learning. More precisely, it assumes that all human infants come into the
world with a linguistic prespecification of the form of possible human grammars. Together with the
ìLanguage Acquisition Device,î which refers to childís strategy for acquiring a grammar, children are
capable of learning any existing languages regardless of their ethical groups and birthplaces (i.e. UG).
Under the theory, the existence of language universals is somewhat truism as UG encompasses possible
human languages. This is the major difference from other quantitative research (e.g. Greenberg 1966).
The elegance of this theory is not only it provides an unconventional view of language univers Recently,
it becomes increasingly popular that evolution of UG is explained by a Baldwinian process (Pinker &
Bloom 1990, Turkel 1996, Dor & Jablonka 2001). However, it is often the case that in such studies,
explanation of the precise mechanism of the Baldwin effect is often compromised. Based on the study of
Yamauchi (2000), in this paper I argue that evolution of UG is aided not by the conventional Baldwin
effect (e.g. Waddington 1975, Hinton and Nowlan 1987), but a new type of the Baldwin effect (Deacon
1997, Godfrey-Smith 2001). Under this type of the Baldwin effect, a population produces its own
linguistic niche. Such a niche is defined only by internally (i.e. external environmental pressure is
minimum), and is constructed by agentsí co-operative activity. This co-operative activity is
characteristic to language evolution as keeping ìparityî among agentsí individual communication systems
is a crucial factor of adaptation. As a new niche is constructed, initially not adaptive gen 

 Yuri Yegorov - Central European University

The Transitions in Industry, Research and Society: Dynamic Equilibrium Approach

 Evolution of industry, science and society exhibit periods of slow development followed by fast
revolutionary changes. The paper is an attempt to model this complex dynamics using the principle of
aggregation of individual optimizing behaviour. Since the subject area represents a mix of equilibrium
and evolution, it calls for new mathematical tools for its modeling. A framework of competition of two
nonconvex technologies (IRS-DRS type) born in different periods allows for possibility to have periods
of their peaceful coexistence with periods of rapid shifts (cascades). A continuum of overlapping
generations allows for external force, which drives temporary equilibrium away if there is an asymmetry
in technologies. The model is formally written for the shift of labor across two sectors. Its results
may be also applied for modeling scientific and ideological revolutions in society. The emergence of new
superior scientific branch may have difficulties in its initial period, caused by impossibilities to
cooperate and possibility to block the superior path by the coalition in the old school. The equilibrium
demand for teaching particular science is also derived: it has endogeneous form of the integral
equation. 

 Igor Yevin - International Association of Empirical Aesthetics

Criticality of the Brain and Criticality of the Art

 Theory of complex system revealed that brain is acting close to instability point and therefore our
brain can respond to any small perturbation, whether intrinsic or extrinsic. This paper aims to show
that art as a complex system also operates near critical point. Using different notions of instability,
borrowed from natural sciences, it will be shown that great many artworks, including well-known
masterpieces, exist near unstable points. For instance, balance - the most important principle of
compositional design in painting, sculpture, and architecture - means creation of unstable states. The
presence of long-range correlations in human writing, painting, and music also indicate that appropriate
works of art are at critical points. Synaesthesia as "intersensory association"(colored hearing
synesthesia is association musical tonalities and colors) could be explained as an ability of input
acoustic signals to access visual part of the brain. It is easy to show that amount of Shannon
information processing between the artwork and the brain riches a maximum magnitude when both the art
and the brain are at critical states. The relationship between "arousal potential" of artworks and
"hedonic value" well known as the Wundt curve (which looks like inverse U) is explained using
Ginzburg-Landau theory of phase transition. The "arousal potential" in this model is the order parameter
and "hedonic value" plays the role of control parameter. The main conclusion of this paper: the art
might be treated as a tool for supporting human brain near criticality. 

 Yi Zhou -
NYU

Detecting and Modeling Long Range Correlation in Genomic Sequences. Yi Zhou, Archisman Rudra, Salvatore
Paxia and Bud Mishra

 A genome encodes information that is needed to create complex machineries combining DNA, RNA and
proteins. However, this structure has evolved by certain basic biological processes that modify the
genome in a specific but stochastic manner, and has been shaped by selection pressure. With complete
sequences of many genomes available, it is now possible to question whether all such genome evolution
processes are adequately understood. In particular, we measure the long-range correlation (LRC) of DNA
sequences in the hope of distinguishing between different models of DNA evolution. In order to study DNA
sequence LRC, we view the DNA sequences as being generated from a random walk model. We map a whole
genomic sequence using a purine-pyrimidine binary rule. This creates a `DNA walk' along the genome. The
degree of LRC in the sequence is characterized by the Hurst exponent (H), which can be estimated using
various methods. (For infinite length: H=0.5, no LRC; H\>0.5, positive LRC; H\<0.5, negative LRC.) We
have analyzed various genomes using VALIS: bacteria, invertebrate and vertebrate. We observe a
consistently higher H value in the non-coding regions compared to the coding regions. Thus, the DNA
walks down the non-coding region sequences possess stronger positive LRC than those in the coding
regions. In addition, the H values in different regions increase with the evolutionary positions of the
corresponding organisms. This suggests that some cellular events tend to make DNA sequences more
positively correlated as evolution proceeds. Based on our observations, we hypothesize that the
differences in the strengths of LRC in DNA sequences are caused by a spectrum of events affecting DNA
evolution. Those include DNA polymerase stuttering, transposons and recombination, which tend to add
deletions and insertions, and natural selection and DNA repair mechanisms, which try to eliminate the
changes in the sequences. The differences in the distribution of such spectrum in coding and non-coding
regions and in different organisms cause the differences in the degree of LRC in DNA sequences. The
hypothesis can be tested 'in silico' using Polya's Urn model. In our model, each basic DNA sequence
change is modeled using several probability distribution functions. The functions can decide the
insertion/deletion positions of the DNA fragments, the copy number of the inserted fragments and the
sequence of the inserted/deleted pieces. Moreover, those functions can be interdependent. The
combination of those basic DNA sequence changes can represent most of the natural DNA evolution events:
deletion, insertion, point mutation, tandem repeats, transposition, etc. Our analysis and simulation
were carried out on two novel computational tools: VALIS, a bioinformatics environment for genome
analysis and `Genome Grammar', a system for simulating genome evolution. Our `Genome Grammar' can handle
stochastic grammars and primitives for many kinds of mathematical probability distributions. It allows
one to apply hypothesized processes on sequences from different sources. In particular, it enables us to
conduct our experiments on DNA evolution based on Polya's Urn model. Finally, the 'in silico'
experimental results can be verified 'in vivo' using microbial mutants in the corresponding cellular
processes. 

 Michal Zochowski - University of Michigan

Optical Imaging of Spatio-Temporal Properties of Odor Evoked Oscillations in the Turtle Olfactory Bulb.
Michal Zochowski LB Cohen

 We made voltage-sensitive dye measurements of the response to several odorants in an in vivo turtle
preparation, using the styryl dye (RH414) and a 464-element photo-diode array (NeuroPlex) to measure
optical signals from the bulb. Four different population signals were detected to odorant stimuli: a
slow depolarization and three oscillations (rostral, middle and caudal). The oscillations had different
spatio-temporal properties - location, frequency and latency. We also applied multiple odorant
presentations with different inter-stimulus intervals, odorant concentrations and odorant combinations.
Two of oscillatory responses change their character dramatically if the second odorant pulse is applied
within 1-20 sec. The rostral oscillation either does not appear or is much smaller in the response to
the second pulse. The caudal oscillation on the other hand, may be partially suppressed and/or not
undergo its period doubling, exhibiting often only a fast 14 Hz frequency. The changes in middle
oscillation depend on the history of odor presentation. If the same odor is presented on the following
odorant pulses as on the first one the middle oscillation is significantly enhanced. On the other hand
if the odorant is different on the following odorant presentations than on the first one this
oscillation is reduced or doesn't appear at all. These changes in the olfactory bulb responses do not
correlate with the changes of magnitude of receptor cell signal, measured by monitoring calcium
concentration in the synapses of the receptor neurons.