DRAFT

Ulric B. and Evelyn L. Bray Social Sciences Seminar

Tuesday, March 1, 2022
4:00pm to 5:00pm
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Baxter Lecture Hall
Information aggregation in social networks
Svetlana Pevnitskaya, Visiting Associate in Economics, Caltech; Associate Professor of Economics, Florida State University,

Abstract: In making choices under uncertainty, individuals often can obtain more information by observing previous decisions of others. We present a theoretical framework of individual choice under uncertainty for agents connected via a directed network which allows for observational learning. The decision is made once, thus learning from repetition is not possible. We obtain properties of networks that affect accuracy of individual choice and information aggregation. Network performance is evaluated using two criteria: individual (final agent) and social (group) choice accuracy, with the result that network properties that enhance performance under one criterion reduce performance under the other. To test theoretical predictions, we design an experiment with two treatment variables: network structure and endogenous/exogenous assignment to positions within a network. In all treatments, there is efficiency loss compared to a benchmark with all Bayesian agents. Despite evidence that individuals understand the value of observational learning, they display a propensity to overweight information inferred from observed actions, which is increasing in the number of actions they observe. We find that in most cases group accuracy predictions conditional on network properties are supported by the data.

For more information, please contact Letty Diaz by email at letty.diaz@caltech.edu.