Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/217086 
Year of Publication: 
2019
Citation: 
[Journal:] Theoretical Economics [ISSN:] 1555-7561 [Volume:] 14 [Issue:] 2 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2019 [Pages:] 403-435
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
I present a model of observational learning with payoff interdependence. Agents, ordered in a sequence, receive private signals about an uncertain state of the world and sample previous actions. Unlike in standard models of observational learning, an agent's payoff depends both on the state and on the actions of others. Agents want both to learn the state and to anticipate others' play. As the sample of previous actions provides information on both dimensions, standard informational externalities are confounded with payoff externalities. I show that in spite of these confounding factors, when signals are of unbounded strength there is learning in a strong sense: agents' actions are ex-post optimal given both the state of the world and others' actions. With bounded signals, actions approach ex-post optimality as the signal structure becomes more informative.
Subjects: 
Observational learning
payoff interdependence
information aggregation
position uncertainty
JEL: 
C72
D83
D85
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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