Strategic Learning With Finite Automata Via The EWA-Lite Model
We modify the self-tuning Experience Weighted Attraction (EWA-lite) model of Camerer, Ho, and Chong (2007) and use it as a computer testbed to study the likely performance of a set of twostate automata in four symmetric 2 x 2 games. The model suggested allows for a richer specification of strategies and solves the inference problem of going from histories to beliefs about opponents' strategies, in a manner consistent with \belief-learning". The predictions are then validated with data from experiments with human subjects. Relative to the action reinforcement benchmark model, our modified EWA-lite model can better account for subject-behavior.
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- Nobuyuki Hanaki & Rajiv Sethi & Ido Erev & Alexander Peterhansl, 2002.
Game Theory and Information
- Camerer, Colin F. & Ho, Teck H. & Chong, Juin-Kuan., 2000. "Sophisticated EWA Learning and Strategic Teaching in Repeated Games," Working Papers 1087, California Institute of Technology, Division of the Humanities and Social Sciences.
- Sebastian J. Goerg & Thorsten Chmura & Reinhard Selten, 2008.
"Learning in experimental 2×2 games,"
Bonn Econ Discussion Papers
bgse18_2008, University of Bonn, Germany.
- Arifovic, Jasmina & McKelvey, Richard D. & Pevnitskaya, Svetlana, 2006. "An initial implementation of the Turing tournament to learning in repeated two-person games," Games and Economic Behavior, Elsevier, vol. 57(1), pages 93-122, October.
- Boylan, Richard T. & El-Gamal, Mahmoud A., 1990. "Fictitious Play: A Statistical Study of Multiple Economic Experiments," Working Papers 737, California Institute of Technology, Division of the Humanities and Social Sciences.
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