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Agent-Based Models

Bibliographic Data

ID6232886
AuthorsScott De Marchi (0000-0003-4355-9800, Duke University), Sarah E Page (0000-0003-2723-5216, University of Michigan), Scott E Page
Year2014
Volume17
Issue1
Pages1-20
Publication date2014-05-11
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueAnnual Review of Political Science (BOOK_SERIES)
Journal identifiersISSN: 1094-2939 • E-ISSN: 1545-1577
PublisherAnnual Reviews (PUBLISHER • US)
DOI10.1146/annurev-polisci-080812-191558
OpenAlexW2140090747
LanguageEN
Citations received44
References cited118

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

Agent-based model · Complement (music · Complex adaptive system · Data science · Economics · Heuristics · Isolation (microbiology · Management science · Mathematical economics · Randomness · Range (aeronautics · Situated · Stochastic game · Computer Science · Engineering · Evolutionary Game Theory and Cooperation · Experimental Behavioral Economics Studies · Mathematics · Opinion Dynamics and Social Influence · Artificial Intelligence

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Unique citing works44
Citations per year2,32
Citation span2007 - 2026 (20)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 42

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