A General Analysis of Sequential Social Learning

Itai Arieli () and Manuel Mueller-Frank ()
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Itai Arieli: Faculty of Industrial Engineering and Management, Technion–Israel Institute of Technology, Haifa 3200003, Israel
Manuel Mueller-Frank: IESE Business School, University of Navarra, 08034 Barcelona, Spain

Mathematics of Operations Research, 2021, vol. 46, issue 4, 1235-1249

Abstract: This paper analyzes a sequential social learning game with a general utility function, state, and action space. We show that asymptotic learning holds for every utility function if and only if signals are totally unbounded, that is, the support of the private posterior probability of every event contains both zero and one. For the case of finitely many actions, we provide a sufficient condition for asymptotic learning depending on the given utility function. Finally, we establish that for the important class of simple utility functions with finitely many actions and states, pairwise unbounded signals, which generally are a strictly weaker notion than unbounded signals, are necessary and sufficient for asymptotic learning.

Keywords: Primary: 91-10; secondary 91B44; Games/group decisions:Noncooperative; asymptotic learning; social learning; unbounded signals; herding (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormoor:v:46:y:2021:i:4:p:1235-1249

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