Essays in Revealed Preference Theory and Behavioral Economics

Abstract

Time, risk, and attention are all integral to economic decision making. The aim of this work is to understand those key components of decision making using a variety of approaches: providing axiomatic characterizations to investigate time discounting, generating measures of visual attention to infer consumers' intentions, and examining data from unique field settings. Chapter 2, co-authored with Federico Echenique and Kota Saito, presents the first revealed-preference characterizations of exponentially-discounted utility model and its generalizations. My characterizations provide non-parametric revealed-preference tests. I apply the tests to data from a recent experiment, and find that the axiomatization delivers new insights on a dataset that had been analyzed by traditional parametric methods. Chapter 3, co-authored with Min Jeong Kang and Colin Camerer, investigates whether pre-choice measures of visual attention improve in prediction of consumers' purchase intentions. We measure participants' visual attention using eyetracking or mousetracking while they make hypothetical as well as real purchase decisions. I find that different patterns of visual attention are associated with hypothetical and real decisions. I then demonstrate that including information on visual attention improves prediction of purchase decisions when attention is measured with mousetracking. Chapter 4 investigates individuals' attitudes towards risk in a high-stakes environment using data from a TV game show, Jeopardy!. I first quantify players' subjective beliefs about answering questions correctly. Using those beliefs in estimation, I find that the representative player is risk averse. I then find that trailing players tend to wager more than folk strategies that are known among the community of contestants and fans, and this tendency is related to their confidence. I also find gender differences: male players take more risk than female players, and even more so when they are competing against two other male players. Chapter 5, co-authored with Colin Camerer, investigates the dynamics of the favorite-longshot bias (FLB) using data on horse race betting from an online exchange that allows bettors to trade in-play. I find that probabilistic forecasts implied by market prices before start of the races are well-calibrated, but the degree of FLB increases significantly as the events approach toward the end.