Overview

Researchers are increasingly turning to computational methods to study the dynamic properties of political and economic systems. Politicians, citizens, interest groups, and organizations interact in dynamic, complex environments, and the static models that are predominant in political economy are limited in capturing fundamental features of economic decision making in modern democracies. Computational models - numerical approximations of equilibria and dynamics that cannot be solved analytically - provide useful insight into the behavior of economic agents and the aggregate properties of political systems. They serve as a valuable complement to existing mathematical tools. This book offers research on computational political economy. The focus is on theoretical models of traditional problems in the field. Each chapter presents an innovative model of interaction between economic agents. Topics include voting behavior, candidate position taking, special interest group contributions, macroeconomic policy making, and corporate decision making.

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9780262112758

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The particular promise of the computational approach to modeling social phenomena is the extent to which it attenuates the need to sacrifice empirical realism for analytical tractability. The papers collected in this volume both exemplify the approach at a very high level and deliver on its promise. As such, the book is both a collection of provocative contributions to our understanding of collective decision-making and a stimulus for further investment in computational models of political economy. --David Austen-Smith, Ethel and John Lindgren Professor of Political Science and Economics, Northwestern University

Author Information

Ken Kollman is Associate Professor of Political Science and Senior Associate Research Scientist at the Center for Political Studies, ISR, at the University of Michigan. John H. Miller is Department Head and Professor of Social and Decision Sciences at Carnegie Mellon University. Scott E. Page is Associate Professor of Political Science, Complex Systems, and Economics at the University of Michigan.

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