Fast convergence in evolutionary equilibrium selection |
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| Institution: | 1. School of Transportation and Logistics, Southwest Jiaotong University, No. 111, Erhuanlu Beiyiduan, Chengdu 610031, PR China;2. Division of Engineering, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi, P.O. Box 129188, United Arab Emirates;3. Department of Civil and Environmental Engineering, University of Michigan Ann Arbor, 2350 Hayward, 2116 GG Brown, Ann Arbor, Michigan 48109-2125, USA;4. Tandon School of Engineering, New York University, Brooklyn, New York, USA |
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| Abstract: | Stochastic best response models provide sharp predictions about equilibrium selection when the noise level is arbitrarily small. The difficulty is that, when the noise is extremely small, it can take an extremely long time for a large population to reach the stochastically stable equilibrium. An important exception arises when players interact locally in small close-knit groups; in this case convergence can be rapid for small noise and an arbitrarily large population. We show that a similar result holds when the population is fully mixed and there is no local interaction. Moreover, the expected waiting times are comparable to those in local interaction models. |
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| Keywords: | Stochastic stability Logit learning Markov chain Convergence time |
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