Learning in networks with idiosyncratic agents

Vatsal Khandelwal

Games and Economic Behavior, 2024, vol. 144, issue C, 225-249

Abstract: Individuals update their beliefs and respond to new information in idiosyncratic ways. I show that an individual's idiosyncrasies such as under-reaction, over-reaction, or frustration can have spillover effects and adversely affect the long run beliefs of society. I derive sufficient conditions for convergence of beliefs for all possible networks of agents with heterogeneous idiosyncrasies. Beliefs converge if the magnitude of over-reaction and frustration in any agent's network neighbourhood is below a threshold determined by how much they trust their own private signals. I find that the absence of disproportionately influential agents is not sufficient to ensure the accuracy of long-run beliefs if learning idiosyncrasies also grow with the network. Finally, I show that agent under-reaction can partition the network, create bottlenecks, and delay convergence. Simulations on artificial and Indian village networks validate the results.

Keywords: Social networks; Learning; Beliefs; Behavioural; DeGroot (search for similar items in EconPapers)
JEL-codes: D80 D83 D85 D90 D91 Z13 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:144:y:2024:i:c:p:225-249

DOI: 10.1016/j.geb.2024.01.010

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