Article

The Potential Value of Computational Models in Social Science Research

Ken Kollman

in The Oxford Handbook of Philosophy of Social Science

Published in print August 2012 | ISBN: 9780195392753
Published online November 2012 | | DOI: http://dx.doi.org/10.1093/oxfordhb/9780195392753.013.0015

Series: Oxford Handbooks

 The Potential Value of Computational Models in Social Science Research

More Like This

Show all results sharing these subjects:

  • Philosophy
  • Philosophy of Science
  • Philosophy of Mathematics and Logic

GO

Show Summary Details

Preview

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

Article.  13188 words. 

Subjects: Philosophy ; Philosophy of Science ; Philosophy of Mathematics and Logic

Full text: subscription required

How to subscribe Recommend to my Librarian

Buy this work at Oxford University Press »

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.