Zhiwei Cui (Zhejiang University & Beijing University of Aeronautics & Astronautics) Jian Zhai (Zhejiang University) Xuan Liu (East Carolina University)
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This paper studies the aspiration-based learning dynamics in symmetric normal-form games played at multiple locations. In particular, the aspiration level in one location is linked to the average performances of players in observable locations. With a decentralized information structure, the learning dynamics converge to limit states. For a large class of information structures and games, when there exists trembles in the updating of aspiration levels, the unique stochastically stable equilibrium is characterized by the Pareto efficient symmetric outcome. In the prisoners' dilemma, if the probability of trembles is sufficiently small, both players in every location will ultimately cooperate most of the time.
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Find related papers by JEL classification: C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General