Social Learning with Payoff Complementarities

Amil Dasgupta
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Amil Dasgupta: Yale University

No 322, Econometric Society World Congress 2000 Contributed Papers from Econometric Society

Abstract: We incorporate strategic complementarities into a multi-agent sequential choice model with observable actions and private information. In this framework agents are concerned with learning from predecessors, signalling to successors, and coordinating their actions with those of others. Coordination problems have hitherto been studied using static coordination games which do not allow for learning behavior. Social learning has been examined using games of sequential action under uncertainty, but in the absence of strategic complementarities (herding models). Our model captures the strategic behavior of static coordination games, the social learning aspect of herding models, and the signalling behavior missing from both of these classes of models in one unified framework. In sequential action problems with incomplete information, agents exhibit herd behavior if later decision makers assign too little importance to their private information, choosing instead to imitate their predecessors. In our setting we demonstrate that agents may exhibit either strong herd behavior (complete imitation) or weak herd behavior (overoptimism) and characterize the informational requirements for these distinct outcomes. We also characterize the informational requirements to ensure the possibility of coordination upon a risky but socially optimal action in a game with finite but unboundedly large numbers of players.

Date: 2000-08-01
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Citations: View citations in EconPapers (23)

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