The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Graphical Games

  • Michael Kearns
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2123

Abstract

Graphical games and related models provide network or graph-theoretic means of succinctly representing strategic interaction among a large population of players. Such models can often have significant algorithmic benefits, as in the NashProp algorithm for computing equilibria. In addition, several studies have established relationships between the topological structure of the underlying network and properties of various outcomes. These include a close relationship between the correlated equilibria of a graphical game and Markov network models for their representation, results establishing when evolutionary stable strategies are preserved in a network setting, and a precise combinatorial characterization of wealth variation in a simple bipartite exchange economy.

Keywords

Computation of Equilibria Correlated Equilibrium Dynamic Programming Evolutionary Game Theory Evolutionary Stable Strategies Exchange Economies Graphical Economics Graphical Games Network Structure 
This is a preview of subscription content, log in to check access

Bibliography

  1. Blume, L.E. 1995. The statistical mechanics of best-response strategy revision. Games and Economic Behavior 11: 111–145.CrossRefGoogle Scholar
  2. Daskalakis, C., and C. Papadimitriou. 2005. Computing pure Nash equilibria via Markov random fields. Proceedings of the 6th ACM conference on electronic commerce.Google Scholar
  3. Daskalakis, C., and C. Papadimitriou. 2006. The complexity of games on highly regular graphs. The 13th annual European symposium on algorithms.Google Scholar
  4. Daskalakis, C., P.W. Goldberg, and C.H. Papadimitriou. 2006. The complexity of computing a Nash equilibrium. Proceedings of the 38th ACM symposium on theory of computing.Google Scholar
  5. Elkind, E., L.A. Goldberg, and P.W. Goldberg. 2006. Nash equilibria in graphical games on trees revisited. Proceedings of the 7th ACM conference on electronic commerce.Google Scholar
  6. Ellison, G. 1993. Learning, local interaction, and coordination. Econometrica 61: 1047–1071.CrossRefGoogle Scholar
  7. Jackson, M. 2007. The study of social networks ineconomics. In The missing links: Formation and decay of economic networks, ed. J. Podolny and J.E. Rauch. New York: Russell Sage Foundation.Google Scholar
  8. Kakade, S., M. Kearns, J. Langford, and L. Ortiz. 2003. Correlated equilibria in graphical games. Proceedings of the 4th ACM conference on electronic commerce.Google Scholar
  9. Kakade, S., M. Kearns, L. Ortiz, R. Pemantle, and S. Suri. 2004a. Economic properties of social networks. Neural information processing systems 18.Google Scholar
  10. Kakade, S., M. Kearns, and L. Ortiz. 2004b. Graphical economics. Proceedings of the 17th conference on computational learning theory.Google Scholar
  11. Kearns, M., and S. Suri. 2006. Networks preserving evolutionary stability and the power of randomization. Proceedings of the 7th ACM conference on electronic commerce.Google Scholar
  12. Kearns, M., M. Littman, and S. Singh. 2001. Graphical models for game theory. Proceedings of the 17th conference uncertainty in artificial intelligence.Google Scholar
  13. Ortiz, L., and M. Kearns. 2002. Nash propagation for loopy graphical games. Neural information systems processing 16.Google Scholar
  14. Papadimitriou, C.. 2005. Computing correlated equilibria in multi-player games. Proceedings of the 37th ACM symposium on the theory of computing.Google Scholar
  15. Schoenebeck, G., and S. Vadhan. 2006. The computational complexity of Nash equilibria in concisely represented games. Proceedings of the 7th ACM conference on electronic commerce.Google Scholar

Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Michael Kearns
    • 1
  1. 1.