Agent-Based Modeling and Complexity

  • Steven M. MansonEmail author
  • Shipeng Sun
  • Dudley Bonsal


Complexity theory provides a common language and rubric for applying agent-based processes to a range of complex systems. Agent-based modeling in turn advances complexity science by actuating many complex system characteristics, such as self-organization, nonlinearity, sensitivity, and resilience. There are many points of contact between complexity and agent-based modeling, and we examine several of particular importance: the range of complexity approaches; tensions between theoretical and empirical research; calibration, verification, and validation; scale; equilibrium and change; and decision making. These issues, together and separately, comprise some of the key issues found at the interface of complexity research and agent-based modeling.


Complexity Research System Entity Attraction Basin Aggregate Complexity Regional Housing Market 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported by the National Aeronautics and Space Administration (NASA) New Investigator Program in Earth-Sun System Science (NNX06AE85G) and the National Science Foundation (0709613). Responsibility for the opinions expressed herein is solely that of the authors.


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© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Department of GeographyUniversity of MinnesotaMinneapolisUSA

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