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Strategic Observational Learning

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  • Dimitri Migrow

Abstract

We study learning by privately informed forward-looking agents in a simple repeated-action setting of social learning. Under a symmetric signal structure, forward-looking agents behave myopically for any degrees of patience. Myopic equilibrium is unique in the class of symmetric threshold strategies, and the simplest symmetric non-monotonic strategies. If the signal structure is asymmetric and the game is infinite, there is no equilibrium in myopic strategies, for any positive degree of patience.

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  • Dimitri Migrow, 2022. "Strategic Observational Learning," Papers 2212.09889, arXiv.org, revised Jan 2023.
    • Handle: RePEc:arx:papers:2212.09889
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    References listed on IDEAS

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    1. Mueller-Frank, Manuel, 2014. "Does one Bayesian make a difference?," Journal of Economic Theory, Elsevier, vol. 154(C), pages 423-452.
    2. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    3. Jussi Keppo & Lones Smith & Dmitry Davydov, 2008. "Optimal Electoral Timing: Exercise Wisely and You May Live Longer -super-1," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 75(2), pages 597-628.
    4. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
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