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Recency, consistent learning, and Nash equilibrium
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DOI：10.1073/pnas.1400987111.png)
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Abstract
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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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