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Learning the fundamentals in a stationary environment

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  • Al-Najjar, Nabil I.
  • Shmaya, Eran

Abstract

A Bayesian agent relies on past observations to learn the structure of a stationary process. We show that the agent's predictions about near-horizon events become arbitrarily close to those he would have made if he knew the long-run empirical frequencies of the process.

Suggested Citation

  • Al-Najjar, Nabil I. & Shmaya, Eran, 2018. "Learning the fundamentals in a stationary environment," Games and Economic Behavior, Elsevier, vol. 109(C), pages 616-624.
    • Handle: RePEc:eee:gamebe:v:109:y:2018:i:c:p:616-624
    DOI: 10.1016/j.geb.2018.02.007
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    References listed on IDEAS

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    1. Kalai, Ehud & Lehrer, Ehud, 1994. "Weak and strong merging of opinions," Journal of Mathematical Economics, Elsevier, vol. 23(1), pages 73-86, January.
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    Cited by:

    1. Al-Najjar, Nabil I. & Shmaya, Eran, 2019. "Recursive utility and parameter uncertainty," Journal of Economic Theory, Elsevier, vol. 181(C), pages 274-288.

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    More about this item

    Keywords

    Learning; Merging; Stationarity;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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