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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021-12-03
摘要
Do boundedly rational players learn to choose equilibrium strategies as they play a game repeatedly? A large literature in behavioral 賽局理論 has proposed and experimentally tested various learning algorithms, but a comparative analysis of their equilibrium convergence properties is lacking. In this paper we analyze Experience-Weighted Attraction (EWA), which generalizes fictitious play, best-response dynamics, reinforcement learning and also replicator dynamics. Studying 2 x 2 games for tractability, we recover some well-known results in the limiting cases in which EWA reduces to the learning rules that it generalizes, but also obtain new results for other parameterizations. For example, we show that in coordination games EWA may only converge to the Pareto-efficient equilibrium, never reach
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