Strategic Learning With Finite Automata Via The EWA-Lite Model
AbstractWe 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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Bibliographic InfoPaper provided by Purdue University, Department of Economics in its series Purdue University Economics Working Papers with number 1269.
Length: 26 pages
Date of creation: Apr 2012
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-04-17 (All new papers)
- NEP-CBE-2012-04-17 (Cognitive & Behavioural Economics)
- NEP-EVO-2012-04-17 (Evolutionary Economics)
- NEP-EXP-2012-04-17 (Experimental Economics)
- NEP-GTH-2012-04-17 (Game Theory)
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