Consensus and Disagreement: Information Aggregation under (not so) Naive Learning

Abhijit Banerjee and Olivier Compte

Papers from arXiv.org

Abstract: We explore a model of non-Bayesian information aggregation in networks. Agents non-cooperatively 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: 2023-11
New Economics Papers: this item is included in nep-gth, nep-mic and nep-net
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http://arxiv.org/pdf/2311.08256 Latest version (application/pdf)

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 (2024)
Working Paper: Consensus and Disagreement: Information Aggregation under (not so) Naive Learning (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2311.08256

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