Recency, consistent learning, and Nash equilibrium
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0027-8424
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Proceedings of the National Academy of Sciences of the United States of America, 2014, Vol. 111, No. 3 Supp., pp. 10826-10829
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FUDENBERG, Drew, LEVINE, David K., Recency, consistent learning, and Nash equilibrium, Proceedings of the National Academy of Sciences of the United States of America, 2014, Vol. 111, No. 3 Supp., pp. 10826-10829 - https://hdl.handle.net/1814/33961
Abstract
We examine the long-term implication of two models of learning with recency bias: recursive weights and limited memory. We show that both models generate similar beliefs and that both have a weighted universal consistency property. Using the limited-memory model we produce learning procedures that both are weighted universally consistent and converge with probability one to strict Nash equilibrium.
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We are grateful to the National Science Foundation (Grants SES-08-51315 and 1258665) for financial support.