Essays on Rational Social Learning

Citation

Huang, Wanying (2024) Essays on Rational Social Learning. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/p1xt-ys43. https://resolver.caltech.edu/CaltechTHESIS:05132024-181232920

Abstract

This dissertation contains three essays, each contributing to the study of social learning among rational agents in various contexts.

In Chapter 1, I study whether individuals can learn the informativeness of their information technology through social learning. Building on the classic sequential social learning model, I introduce the possibility that a common source is completely uninformative. I then define asymptotic learning as the situation in which an outsider, who observes the actions of all agents, eventually distinguishes between uninformative and informative sources. I show that asymptotic learning in this setting is not guaranteed; it depends crucially on the relative tail distributions of private beliefs induced by uninformative and informative signals. Furthermore, I identify the phenomenon of perpetual disagreement as the cause of learning and provide a characterization of learning in the canonical Gaussian environment.

In Chapter 2, co-authored with Omer Tamuz and Philipp Strack, we study the asymptotic rate at which the probability of a group of long-lived, rational agents in a social network taking the correct action converges to one. In every period, after observing the past actions of his neighbors, each agent receives a private signal, and chooses an action whose payoff depends only on the state. Since equilibrium actions depend on higher-order beliefs, characterizing agents' behavior becomes difficult. Nevertheless, we show that the rate of learning in any equilibrium is bounded from above by a constant, regardless of the size and shape of the network, the utility function, and the patience of the agents. This bound only depends on the private signal distribution.

In Chapter 3, I study how fads emerge from social learning in a changing environment. I consider a simple sequential learning model in which rational agents arrive in order, each acting only once, and the underlying unknown state is constantly evolving. Each agent receives a private signal, observes all past actions of others, and chooses an action to match the current state. Because the state changes over time, cascades cannot last forever, and actions also fluctuate. I show that despite the rise of temporary information cascades, in the long run, actions change more often than the state. This provides a theoretical foundation for faddish behavior in which people often change their actions more frequently than necessary.

Thesis Files