Agent-Based Models

Posted: 21 May 2014

See all articles by Scott de Marchi

Scott de Marchi

Duke University

Scott E. Page

University of Michigan at Ann Arbor - Department of Physics

Date Written: May 2014

Abstract

Agent-based models (ABMs) provide a methodology to explore systems of interacting, adaptive, diverse, spatially situated actors. Outcomes in ABMs can be equilibrium points, equilibrium distributions, cycles, randomness, or complex patterns; these outcomes are not directly determined by assumptions but instead emerge from the interactions of actors in the model. These behaviors may range from rational and payoff-maximizing strategies to rules that mimic heuristics identified by cognitive science. Agent-based techniques can be applied in isolation to create high-fidelity models and to explore new questions using simple constructions. They can also be used as a complement to deductive techniques. Overall, ABMs offer the potential to advance social sciences and to help us better understand our complex world.

Suggested Citation

de Marchi, Scott and Page, Scott E., Agent-Based Models (May 2014). Annual Review of Political Science, Vol. 17, pp. 1-20, 2014. Available at SSRN: https://ssrn.com/abstract=2439664 or http://dx.doi.org/10.1146/annurev-polisci-080812-191558

Scott De Marchi (Contact Author)

Duke University ( email )

100 Fuqua Drive
Durham, NC 27708-0204
United States

Scott E. Page

University of Michigan at Ann Arbor - Department of Physics ( email )

Ann Arbor, MI
United States

Register to save articles to
your library

Register

Paper statistics

Abstract Views
501
PlumX Metrics