Observational Learning with Position Uncertainty
AbstractObservational 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 InfoPaper provided by Collegio Carlo Alberto in its series Carlo Alberto Notebooks with number 206.
Length: 39 pages
Date of creation: 2011
Date of revision:
social learning; information aggregation; herds; position uncertainty; observational learning;
Find related papers by JEL classification:
- C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-05-24 (All new papers)
- NEP-CBA-2011-05-24 (Central Banking)
- NEP-CTA-2011-05-24 (Contract Theory & Applications)
- NEP-GTH-2011-05-24 (Game Theory)
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- Antonio Guarino & Philippe Jehie, 2009.
"Social Learning with Coarse Inference,"
Levine's Working Paper Archive
814577000000000292, David K. Levine.
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