Herd Design

Author:

Arieli Itai1,Gradwohl Ronen2,Smorodinsky Rann1

Affiliation:

1. Faculty of Data and Decision Sciences, Technion (email: )

2. Department of Economics and Business Administration, Ariel University (email: )

Abstract

The classic herding model examines the asymptotic behavior of agents who observe their predecessors’ actions as well as a private signal from an exogenous information structure. In this paper, we introduce a self-interested sender into the model and study her problem of designing this information structure. If agents cannot observe each other, the model reduces to Bayesian persuasion. However, when agents observe predecessors’ actions, they may learn from them, potentially harming the sender. We identify necessary and sufficient conditions under which the sender can nevertheless obtain the same utility as when the agents are unable to observe each other. (JEL D82, D83, D91)

Publisher

American Economic Association

Subject

Management, Monitoring, Policy and Law,Geography, Planning and Development

Reference26 articles.

1. Bayesian Learning in Social Networks

2. Identifiable information structures

3. Arieli, Itai, Fedor Sandomirskiy, and Rann Smorodinsky. 2020. "On Social Networks That Support Learning." Unpublished.

4. Dynamic information disclosure

5. Aumann, Robert J., Michael B. Maschler, and Richard E. Stearns. 1995. Repeated Games with Incomplete Information. Cambridge, MA: MIT Press.

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