Artikel

Network structure and naive sequential learning

We study a sequential-learning model featuring a network of naive agents with Gaussian information structures. Agents apply a heuristic rule to aggregate predecessors' actions. They weigh these actions according the strengths of their social connections to different predecessors. We show this rule arises endogenously when agents wrongly believe others act solely on private information and thus neglect redundancies among observations. We provide a simple linear formula expressing agents' actions in terms of network paths and use this formula to characterize the set of networks where naive agents eventually learn correctly. This characterization implies that, on all networks where later agents observe more than one neighbor, there exist disproportionately influential early agents who can cause herding on incorrect actions. Going beyond existing social-learning results, we compute the probability of such mislearning exactly. This allows us to compare likelihoods of incorrect herding, and hence expected welfare losses, across network structures. The probability of mislearning increases when link densities are higher and when networks are more integrated. In partially segregated networks, divergent early signals can lead to persistent disagreement between groups.

Language: Englisch

Bibliographic citation: Journal: Theoretical Economics ; ISSN: 1555-7561 ; Volume: 15 ; Year: 2020 ; Issue: 2 ; Pages: 415-444 ; New Haven, CT: The Econometric Society

Classification: Wirtschaft
Network Formation and Analysis: Theory
Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
Micro-Based Behavioral Economics: General‡

Subject: Network structure
sequential social learning
naive inference
mislearning
disagreement

Event: Geistige Schöpfung

(who): Dasaratha, Krishna
He, Kevin

Event: Veröffentlichung

(who): The Econometric Society

(where): New Haven, CT

(when): 2020

DOI: doi:10.3982/TE3388

Handle: http://hdl.handle.net/10419/253460

Last update: 10.03.2025, 11:42 AM CET

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Artikel

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Dasaratha, Krishna
He, Kevin
The Econometric Society

Time of origin

2020

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Network structure and naive sequential learning

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Other Objects (12)

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