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International Conference on Complex Systems (ICCS2006)

Multi-Agent Based Simulation of a Model of Complexity Leadership

Russ Marion
Clemson University

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     Last modified: April 27, 2006

Multi-Agent Based Simulation of a Model of Complexity Leadership

Craig Schreiber, Carnegie-Melon
Russ Marion, Clemson University
Mary Uhl-Bien, University of Central Florida
Kathleen Carley, Carnegie-Melon

March 30, 2006
Paper presented for presentation at the 2006 International Conference on Complex Systems, Boston, MA

This paper reports the results of a multi-agent based simulation experiment that explores a leadership model developed by Uhl-Bien, Marion, and McKelvey (under review). The leadership model (called the Complexity Leadership model) draws from complexity theory to posit three leadership functions: managerial, enabling, and adaptive.

Adaptive leadership refers to change events that occur in the “spaces between” informally interacting individuals (Drath 2001; Lichtenstein et al. 2006). It is premised on the assumption that leadership creates or fosters change—a premise that is consistent with much of the historical literature on the subject (Bryman 1996). Adaptive leadership, then, manifests as change events that emerge from informal interactions among individuals and within groups. Because it derives from informal interactions, its expression is independent of role and authority.

Informal change events of this sort are the foundation of dynamic organizational learning, innovation, and adaptability (Uhl-Bien et al.). However, if the ideas that adaptive leadership produce are to evolve, improve, differentiate, and impact the larger organization, they must be couched within a complex social network. Such networks are characterized by such things as interactions, interdependencies, and tension (when ideas conflict). According to Kauffman (Kauffman 1993), these networks enable ideas to combine, diverge, mutate, and migrate, thus fostering creative emergence of higher order innovations (Marion 1999).

Enabling leadership fosters conditions that encourage adaptive leadership and that create network conditions which enable learning, innovation, and adaptability to emerge from complex interactions. Enabling leadership promotes complex networks by enabling such things as interaction (Schreiber 2005), interdependency, adaptive tension (McKelvey 2004), knowledge variety (or heterogeneous skills; see McKelvey, 2004), heterogeneous vision, and adaptive rules (Bonabeau and Meyer 2001).

Finally, managerial leadership fulfills traditional executive roles, including strategic leadership (Marion and Uhl-Bien in press), visioning, coordination, resource allocation, and environmental negotiations. The three leadership functions—adaptive, enabling, and managerial—are interdependently entwined in the Complexity Leadership model.

Using the multi-agent based simulation model called Construct (Carley 1990; Schreiber, Singh and Carley 2004), the researchers assume an enabling leadership role and explore strategies for fostering adaptive behaviors and emergent outcomes. Specifically, we examine the effects of knowledge variety, shared versus heterogeneous vision (among agents), and interdependency (among agents) on organizational learning and interaction. These variables will be examined with varying group sizes (50, 250, and 750), varying levels of shared vision (25%, 50%, and 75%; larger numbers indicate greater shared visioning and less heterogeneity of vision), varying levels of knowledge heterogeneity (group size X2, X3, X4; larger products indicate higher levels of knowledge heterogeneity), and varying levels of interdependency (25%, 50%, and 75%; higher values indicate greater interdependency, as in Kauffman’s. 1993, k value). We propose, consistent with complexity theory (Marion, 1999), that optimal organizational learning and interaction will occur at moderate levels of shared visioning and interdependency.
We are particularly interested in understanding the mechanisms (Hedström and Swedberg 1998) by which organizational learning occurs. We will examine, for example, structural characteristics that emerge among agents in learning organizations, the most effective balance between coordination (degree of shared vision) and unstructured activity, emerging patterns of shared and unshared visioning, the patterns of interaction, and interactions among various variables.

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McKelvey, B., 2004, "MicroStrategy from MacroLeadership: Distributed intelligence via new science," in A. Y. Lewin and H. Volberda, eds, Mobilizing the self-renewing organization, M. E. Sharp., Armonk, NY.
Schreiber, C., Singh, S., & Carley, K. M., 2004. "Construct - A multi-agent network model for the co-evolution of agents and socio-cultural environments," Carnegie Mellon University, School of Computer Science, Institute for Software Research, International. Technical Report, CMU-ISRI-04-109.
Schreiber, C., & Carley, K. M., 2005, "Ineffective organizational practices at NASA: A dynamic network analysis," Carnegie Mellon University, School of Computer Science, Institute for Software Research, International. Technical Report, CMU-ISRI-05-135.
Uhl-Bien, M., R. Marion, and B. McKelvey, Under review, "Complexity Leadership Theory: Shifting Leadership from the Industrial Age to the Knowledge Era," The Leadership Quarterly.

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