Abstract and Keywords
This article presents intellectual context for computational modeling, namely the manner in which it fits into the collective enterprise of advancing modern social science theory, and also assesses the claims made by critics and proponents of computational modeling in the social sciences, with a special focus on complexity models. Prediction takes a back seat for the most influential mathematical models from the social sciences. Computational models can be tools for explanation. Inductive-statistical (IS), deductive-nomological (DN), causal-mechanical (CM), and causally relevant (CR) explanations have both insight and prediction as elements of scientific explanation, though they vary in their emphases. The article then turns to computational models, and specifically models in the complexity tradition. There is little doubt that computational models permit the analysis of the aggregation of behaviors of diverse, adaptive agents which might alter their decision rules in response to aggregate patterns better than other modeling methods, especially game theoretic models.
Keywords: computational modeling, modern social science, complexity models, computational models, scientific explanation
Access to the complete content on Oxford Handbooks Online requires a subscription or purchase. Public users are able to search the site and view the abstracts and keywords for each book and chapter without a subscription.
Please subscribe or login to access full text content.
If you have purchased a print title that contains an access token, please see the token for information about how to register your code.
For questions on access or troubleshooting, please check our FAQs, and if you can''t find the answer there, please contact us.
