Information Aggregation in Overlapping Generations

26 Pages Posted: 13 Sep 2017

See all articles by Mohammad Akbarpour

Mohammad Akbarpour

Stanford University

Amin Saberi

Stanford University - Management Science & Engineering

Ali Shameli

Stanford University, School of Engineering, Management Science & Engineering, Students

Date Written: September 1, 2017

Abstract

We study a model of social learning with overlapping generations, where agents meet others and share data about an underlying state over time. We examine under what conditions the society will produce individuals with precise knowledge about the state of the world. Under the full information sharing technology, individuals exchange the information about their point estimates of the underlying state, as well as the precision of their signals and update their beliefs accordingly. Under the limited information sharing technology, agents observe the point estimates but not precisions, and update their beliefs by taking a weighted average, where weights can depend on the sequence of meetings, as well as the ‘age’ and the number of previous meetings an agent has had. Our main result shows that, unlike static settings, using linear learning rules without access to the precision information will not guide the population (or even a fraction of its members) to converge to a unique belief, and having access to the precision of a source signal is essential for having an informed population.

Keywords: Information aggregation, word-of-mouth, social learning, precision, overlapping generations

JEL Classification: D83

Suggested Citation

Akbarpour, Mohammad and Saberi, Amin and Shameli, Ali, Information Aggregation in Overlapping Generations (September 1, 2017). Available at SSRN: https://ssrn.com/abstract=3035178 or http://dx.doi.org/10.2139/ssrn.3035178

Mohammad Akbarpour (Contact Author)

Stanford University ( email )

Amin Saberi

Stanford University - Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

Ali Shameli

Stanford University, School of Engineering, Management Science & Engineering, Students ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

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