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Observational Learning with Position Uncertainty

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  • Ignacio Monzon
  • Michael Rapp

Abstract

Observational learning is typically examined when agents have precise information about their position in the sequence of play. We present a model in which agents are uncertain about their positions. Agents are allowed to have arbitrary ex-ante beliefs about their positions: they may observe their position perfectly, imperfectly, or not at all. Agents sample the decisions of past individuals and receive a private signal about the state of the world. We show that social learning is robust to position uncertainty. Under any sampling rule satisfying a stationarity assumption, learning is complete if signal strength is unbounded. In cases with bounded signal strength, we show that agents achieve what we define as constrained efficient learning: individuals do at least as well as the most informed agent would do in isolation.

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Bibliographic Info

Paper provided by Collegio Carlo Alberto in its series Carlo Alberto Notebooks with number 206.

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Length: 39 pages
Date of creation: 2011
Date of revision:
Handle: RePEc:cca:wpaper:206

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Related research

Keywords: social learning; information aggregation; herds; position uncertainty; observational learning;

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Cited by:
  1. Antonio Guarino & Philippe Jehie, 2009. "Social Learning with Coarse Inference," Levine's Working Paper Archive 814577000000000292, David K. Levine.

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