Consensus and Disagreement: Information Aggregation under (Not So) Naive Learning

Abhijit Banerjee and Olivier Compte ()
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Olivier Compte: PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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Abstract: We explore a model of non-Bayesian information aggregation in networks. Agents noncooperatively choose among Friedkin-Johnsen-type aggregation rules to maximize payoffs. The DeGroot rule is chosen in equilibrium if and only if there is noiseless information transmission, leading to consensus. With noisy transmission, while some disagreement is inevitable, the optimal choice of rule amplifies the disagreement: even with little noise, individuals place substantial weight on their own initial opinion in every period, exacerbating the disagreement. We use this framework to think about equilibrium versus socially efficient choice of rules and its connection to polarization of opinions across groups.

Date: 2024-08
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Published in Journal of Political Economy, 2024, 132 (8), pp.2790-2829. ⟨10.1086/729448⟩

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Related works:
Journal Article: Consensus and Disagreement: Information Aggregation under (Not So) Naive Learning (2024)
Working Paper: Consensus and Disagreement: Information Aggregation under (Not So) Naive Learning (2024)
Working Paper: Consensus and Disagreement: Information Aggregation under (not so) Naive Learning (2023)
Working Paper: Consensus and Disagreement: Information Aggregation under (not so) Naive Learning (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:pseptp:halshs-04806668

DOI: 10.1086/729448

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