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Consensus and Disagreement: Information Aggregation under (not so) Naive Learning / Abhijit Banerjee, Olivier Compte.
- Format:
- Book
- Author/Creator:
- Banerjee, Abhijit.
- Series:
- Working Paper Series (National Bureau of Economic Research) no. w29897.
- NBER working paper series no. w29897
- Language:
- English
- Physical Description:
- 1 online resource: illustrations (black and white);
- Place of Publication:
- Cambridge, Mass. National Bureau of Economic Research 2022.
- Summary:
- 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 blows up disagreement: even with little noise, individuals place substantial weight on their own initial opinion in every period, which inflates 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.
- Notes:
- Print version record
- April 2022.
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