Bayesian Social Learning with Local Interactions
AbstractWe study social learning in a large population of agents who only observe the actions taken by their neighbours. Agents have to choose one, out of two, reversible actions, each optimal in one, out of two, unknown states of the world. Each agent chooses rationally, on the basis of private information and of the observation of his neighbours’ actions. Agents can repeatedly update their choices at revision opportunities that they receive in a random sequential order. We show that if agents receive equally informative signals and observe both neighbours, then actions converge exponentially fast to a configuration where some agents are permanently wrong. In contrast, if agents are unequally informed (in that some agents receive a perfectly informative signal and others are uninformed) and observe one neighbour only, then everyone will eventually choose the correct action. Convergence, however, obtains very slowly, at rate √t.
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Bibliographic InfoArticle provided by MDPI, Open Access Journal in its journal Games.
Volume (Year): 1 (2010)
Issue (Month): 4 (October)
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Web page: http://www.mdpi.com/
social learning; Bayesian learning; local informational externalities; path dependence; consensus; clustering; convergence rates;
Find related papers by JEL classification:
- C - Mathematical and Quantitative Methods
- C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
- C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
- C71 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Cooperative Games
- C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
- C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
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