Paper 1109 Strategic Learning With Finite Automata
Via The EWA-Lite Model
Author: Christos A. Ioannou (University of Southampton)
Julian Romero (Purdue University)
Abstract
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 probable 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