A General Analysis of Sequential Social Learning

23 Pages Posted: 17 Jul 2014 Last revised: 9 Aug 2019

See all articles by Itai Arieli

Itai Arieli

Technion-Israel Institute of Technology

Manuel Mueller-Frank

University of Navarra, IESE Business School

Date Written: February 21, 2019

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, i.e., 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 is a strictly weaker notion than unbounded signals, are sufficient for asymptotic learning.

Keywords: Social Learning, Information Cascades, Adequate Learning, Asymptotic Learning, Totally Unbounded Signals

Suggested Citation

Arieli, Itai and Mueller-Frank, Manuel, A General Analysis of Sequential Social Learning (February 21, 2019). IESE Business School Working Paper No. WP-1119-E. Available at SSRN: https://ssrn.com/abstract=2466903 or http://dx.doi.org/10.2139/ssrn.2466903

Itai Arieli (Contact Author)

Technion-Israel Institute of Technology ( email )

Technion City
Haifa 32000, Haifa 32000
Israel

Manuel Mueller-Frank

University of Navarra, IESE Business School ( email )

Avenida Pearson 21
Barcelona, 08034
Spain

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