Levels of Complexity: Endogenizing Agent-Based Modeling

Lars-Erik Cederman

In Social Agents: Ecology, Exchange, and Evolution, eds. Charles Macal and David Sallach. Lemont, IL: Argonne National Laboratory, 175–84.

Agent‐based modeling is a computational methodology that allows scientists to create, analyze, and experiment with artificial worlds populated by agents that interact in non‐trivial ways and that constitute their own environment. In these "complex adaptive systems," computation is used to simulate agents’ cognitive processes and behavior inorder to explore emergent macro phenomena (i.e., structural patterns that are not reducible to, or even understandable in terms of, properties of the micro‐level agents). Such models typically feature local and dispersed interaction rather than centralized control (Resnick, 1994). Moreover, as opposed to conventional rational‐choice models that assume either a small number of dissimilar or numerous identical actors, agent‐based models normally include large numbers of heterogeneous agents. Rather than studying equilibrium behavior, the focus is often on dynamics and transient trajectories far from equilibrium. Finally, instead of assuming the environment to be fixed, many agent‐based models let the agents constitute their own endogenous environment.

URL: http://www.ipd.anl.gov/anlpubs/2003/04/46046.pdf

DOI:

Cederman, Lars-Erik. 2002. “Levels of Complexity: Endogenizing Agent-Based Modeling.” In Social Agents: Ecology, Exchange, and Evolution, eds. Charles Macal and David Sallach. Lemont, IL: Argonne National Laboratory, 175–84.

@inbook{levels-of-complexity,
   Author = {Cederman, Lars-Erik},
   title = {Levels of Complexity: Endogenizing Agent-Based Modeling},
   booktitle = {Social Agents: Ecology, Exchange, and Evolution},
   editor = {Macal, Charles and Sallach, David},
   isbn = {0967916836},
   year = {2002},
   pages = {175--184},
   address = {Lemont, IL},
   publisher = {Argonne National Laboratory},
   abstract = {Agent-based modeling is a computational methodology that allows scientists to create, analyze, and experiment with artificial worlds populated by agents that interact in non-trivial ways and that constitute their own environment. In these "complex adaptive systems," computation is used to simulate agents' cognitive processes and behavior inorder to explore emergent macro phenomena (i.e., structural patterns that are not reducible to, or even understandable in terms of, properties of the micro-level agents). Such models typically feature local and dispersed interaction rather than centralized control (Resnick, 1994). Moreover, as opposed to conventional rational-choice models that assume either a small number of dissimilar or numerous identical actors, agent-based models normally include large numbers of heterogeneous agents. Rather than studying equilibrium behavior, the focus is often on dynamics and transient trajectories far from equilibrium. Finally, instead of assuming the environment to be fixed, many agent-based models let the agents constitute their own endogenous environment.},
   doi = {},
   url = {http://www.ipd.anl.gov/anlpubs/2003/04/46046.pdf},
   status = {personal}
}