Artikel
A general framework for rational learning in social networks
This paper provides a formal characterization of the process of rational learning in social networks. Agents receive initial private information and select an action out of a choice set under uncertainty in each of infinitely many periods, observing the history of choices of their neighbors. Choices are made based on a common behavioral rule. Conditions under which rational learning leads to global consensus, local indifference and local disagreement are characterized. In the general setting considered, rational learning can lead to pairs of neighbors selecting different actions once learning ends, while not being indifferent among their choices. The effect of the network structure on the degree of information aggregation and speed of convergence is also considered and an answer to the question of optimal information aggregation in networks provided. The results highlight distinguishing features between properties of Bayesian and non-Bayesian learning in social networks.
- Language
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Englisch
- Bibliographic citation
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Journal: Theoretical Economics ; ISSN: 1555-7561 ; Volume: 8 ; Year: 2013 ; Issue: 1 ; Pages: 1-40 ; New Haven, CT: The Econometric Society
- Classification
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Wirtschaft
Asymmetric and Private Information; Mechanism Design
Asymmetric and Private Information; Mechanism Design
Network Formation and Analysis: Theory
- Subject
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Learning
social networks
common knowledge
consensus
speed of convergence
optimal information aggregation
- Event
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Geistige Schöpfung
- (who)
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Mueller-Frank, Manuel
- Event
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Veröffentlichung
- (who)
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The Econometric Society
- (where)
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New Haven, CT
- (when)
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2013
- DOI
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doi:10.3982/TE1015
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Mueller-Frank, Manuel
- The Econometric Society
Time of origin
- 2013
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