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International Conference on Complex Systems (ICCS2006)

State-based Reconstructability Analysis

Michael S. Johnson
Systems Science PhD Program, Portland State University

Martin Zwick
Systems Science PhD Program, Portland State University

     Full text: Not available
     Last modified: May 18, 2006

Abstract
Established methods for modeling multivariate systems described by categorical data include variable-based reconstructability analysis and log linear analysis. These methods are variable-based in the sense that the system constraint is described in terms of the variables comprising the system and any interactions among those variables. State-based modeling extends these established methods by permitting models defined in terms of system states, i.e., combinations of categories for subsets of the variables comprising the system.

If the constraint associated with a multivariate interaction is localized to just a few system states, a state-based model can more precisely describe the nature of that constraint while using fewer parameters than a variable-based model. For confirmatory modeling this results in more powerful hypothesis tests.

In the context of exploratory modeling, state-based modeling greatly expands the lattice of possible system models both vertically, by enabling more granular changes in model complexity, and horizontally, by increasing the number of possible models at each level of complexity. Consequently, investigators have a much richer set of candidate models to consider, but also face challenges with respect to searching the model lattice efficiently and distinguishing real system structure from sampling artifacts.

This presentation outlines recent research that has established a conceptual framework for state-based modeling and provided computer software to support specification, fitting, and testing of such models. Problems of model estimation and the determination of model complexity are addressed.




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