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Published June 1998 | public
Journal Article

Quantal Response Equilibria for Extensive Form Games

Abstract

This article investigates the use of standard econometric models for quantal choice to study equilibria of extensive form games. Players make choices based on a quantal-choice model and assume other players do so as well. We define an agent quantal response equilibrium (AQRE), which applies QRE to the agent normal form of an extensive form game and imposes a statistical version of sequential rationality. We also define a parametric specification, called logit-AQRE, in which quantal-choice probabilities are given by logit response functions. AQRE makes predictions that contradict the invariance principle in systematic ways. We show that these predictions match up with some experimental findings by Schotter et al. (1994) about the play of games that differ only with respect to inessential transformations of the extensive form. The logit-AQRE also implies a unique selection from the set of sequential equilibria in generic extensive form games. We examine data from signaling game experiments by Banks et al. (1994) and Brandts and Holt (1993). We find that the logit-AQRE selection applied to these games succeeds in predicting patterns of behavior observed in these experiments, even when our prediction conflicts with more standard equilibrium refinements, such as the intuitive criterion. We also reexamine data from the McKelvey and Palfrey (1992) centipede experiment and find that the AQRE model can account for behavior that had previously been explained in terms of altruistic behavior.

Additional Information

© 1998 Economic Science Association. This article has benefited from comments by two referees and by several participants at the Conference on Learning in Games (Texas A&M University, February 1994) and the 1995 Summer Workshop on Game theory at Stonybrook, and the European Meetings of the Econometric Society (Maastricht, September 1994). We thank the participants for their comments and also thank Peter Coughlan, Mark Fey, Eugene Grayver, and Rob Weber for their research assistance. We are grateful to Andy Schotter, David Porter, and Charles Holt for sharing their data. The financial support of the National Science Foundation (Grant #SBR-9223701) is gratefully acknowledged.

Additional details

Created:
August 19, 2023
Modified:
October 25, 2023