Artikel
Fragility of asymptotic agreement under Bayesian learning
Under the assumption that individuals know the conditional distributions of signals given the payoff-relevant parameters, existing results conclude that as individuals observe infinitely many signals, their beliefs about the parameters will eventually merge. We first show that these results are fragile when individuals are uncertain about the signal distributions: given any such model, vanishingly small individual uncertainty about the signal distributions can lead to substantial (non-vanishing) differences in asymptotic beliefs. Under a uniform convergence assumption, we then characterize the conditions under which a small amount of uncertainty leads to significant asymptotic disagreement.
- Language
-
Englisch
- Bibliographic citation
-
Journal: Theoretical Economics ; ISSN: 1555-7561 ; Volume: 11 ; Year: 2016 ; Issue: 1 ; Pages: 187-225 ; New Haven, CT: The Econometric Society
- Classification
-
Wirtschaft
Bayesian Analysis: General
Noncooperative Games
Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
- Subject
-
Asymptotic disagreement
Bayesian learning
merging of opinions
- Event
-
Geistige Schöpfung
- (who)
-
Yildiz, Muhamet
Acemoglu, Daron
Chernozhukov, Victor
- Event
-
Veröffentlichung
- (who)
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The Econometric Society
- (where)
-
New Haven, CT
- (when)
-
2016
- DOI
-
doi:10.3982/TE436
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the 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
- Yildiz, Muhamet
- Acemoglu, Daron
- Chernozhukov, Victor
- The Econometric Society
Time of origin
- 2016
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