Essays in Behavioral Economics

Citation

Zeidel, Jeffrey Roy (2023) Essays in Behavioral Economics. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/779z-c728. https://resolver.caltech.edu/CaltechTHESIS:06012023-230351408

Abstract

This dissertation contains three essays in three chapters. Chapter 1 contributes to the literature on reference dependent preferences, chapter 2 introduces a new solution concept for games played by teams of players, and chapter 3 analyzes a model of biased beliefs in law enforcement.

In Chapter 1, I study the role of reference dependent preferences in motivating effort in online chess. In online chess, players are assigned ratings that measure chess skill and update after every game. I find evidence of bunching above round numbers in the distribution of ratings, suggesting that players care about their rating and that round numbers serve as reference points. I estimate a dynamic discrete choice model of the decision to end a playing session that nests both loss aversion and an alternative 'aspiration' specification involving a discrete jump in utility at reference points. I reject loss aversion in favor of aspirational preferences. I show that higher skilled players are significantly more aspirational, and that aspiration does not diminish with experience.

In Chapter 2, coauthored with Jeongbin Kim and Thomas R. Palfrey, we develop a general framework for the analysis of games where each player is a team and members of the same team all receive the same payoff. The framework combines standard non-cooperative game theory with collective choice theory, and is developed for both strategic form and extensive form games. We introduce the concept of team equilibrium and identify conditions under which it converges to Nash equilibrium with large teams. We identify conditions on the collective choice rules such that team decisions are stochastically optimal: the probability the team chooses an action is increasing in its equilibrium expected payoff. The theory is illustrated with some binary action games.

In Chapter 3, I model a social welfare maximizing law enforcement agency that does not know the supply of crime, that may have incorrect beliefs about its ability to detect crime, and that only observes the quantity of crime that it detects. An equilibrium is defined in which the enforcement agency is not surprised by the crime data it observes, and believes itself to be maximizing social welfare. Sufficient conditions for existence are provided. The model is shown to capture the intuition of crime-policing 'feedback loops' in which inefficient overpolicing or underpolicing is supported in equilibrium.

Thesis Files