Please use this identifier to cite or link to this item:
Guarino, Antonio
Ianni, Antonella
Year of Publication: 
[Journal:] Games [ISSN:] 2073-4336 [Publisher:] MDPI [Place:] Basel [Volume:] 1 [Year:] 2010 [Issue:] 4 [Pages:] 438-458
We 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.
social learning
Bayesian learning
local informational externalities
path dependence
convergence rates
Persistent Identifier of the first edition: 
Creative Commons License:
Document Type: 
Appears in Collections:

Files in This Item:
330.64 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.