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Stochastic Stability of a Recency Weighted Sampling Dynamic

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  • Alexander Aurell
  • Gustav Karreskog

Abstract

We introduce and study a model of long-run convention formation for rare interactions. Players in this model form beliefs by observing a recency-weighted sample of past interactions, to which they noisily best respond. We propose a continuous state Markov model, well-suited for our setting, and develop a methodology that is relevant for a larger class of similar learning models. We show that the model admits a unique asymptotic distribution which concentrates its mass on some minimal CURB block configuration. In contrast to existing literature of long-run convention formation, we focus on behavior inside minimal CURB blocks and provide conditions for convergence to (approximate) mixed equilibria conventions inside minimal CURB blocks.

Suggested Citation

  • Alexander Aurell & Gustav Karreskog, 2020. "Stochastic Stability of a Recency Weighted Sampling Dynamic," Papers 2009.12910, arXiv.org, revised Jun 2021.
    • Handle: RePEc:arx:papers:2009.12910
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    References listed on IDEAS

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    7. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, April.
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