Social learning with local interactions

Guarino, Antonio and Ianni, Antonella (2010) Social learning with local interactions. , University of Southampton (Discussion Papers in Economics and Econometrics 1011).


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We study a simple dynamic model of social learning with local informational exter-nalities. There is a large population of agents, who repeatedly have to choose one, out of two, reversible actions, each of which is optimal in one, out of two, unknown states of the world. Each agent chooses rationally, on the basis of private information (s)he receives by a symmetric binary signal on the state, as well as the observation of the action chosen among their nearest neighbours. Actions can be updated at revision opportunities that agents receive in a random sequential order. Strategies are stationary, in that they do not depend on time, nor on location.

We show that: if agents receive equally informative signals, and observe both neighbours, then the social learning process is not adequate and the process of actions converges ex-ponentially fast to a con?guration where some agents are permanently wrong; if agents are unequally informed, in that their signal is either fully informative or fully uninformative (both with positive probability), and observe one neighbour, then the social learning process is adequate and everybody will eventually choose the action that is correct given the state. Convergence, however, obtains very slowly, namely at rate pt:

We relate the?findings with the literature on social learning and discuss the property of effciency of the information transmission mechanism under local interaction.

Item Type: Monograph (Discussion Paper)
ISSNs: 0966-4246 (electronic)
Keywords: social learning, bayesian learning, local informational external-ities, path dependence, consensus, clustering, convergence Rates
Subjects: H Social Sciences > HM Sociology
H Social Sciences > HV Social pathology. Social and public welfare
L Education > L Education (General)
Divisions : University Structure - Pre August 2011 > School of Social Sciences > Economics
ePrint ID: 161633
Accepted Date and Publication Date:
11 June 2010Published
Date Deposited: 03 Aug 2010 09:09
Last Modified: 31 Mar 2016 13:28
Reinforcement Learning: Analytical Results and Methodology for Estimation
Funded by: ESRC National Centre for Research Methods (R000223704)
January 2002 to December 2003

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