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Private information is at the heart of many economic activities. For decades, economists have assumed that individuals are willing to misreport private information if this maximizes their material payoff. We combine data from 90 experimental studies in economics, psychology, and sociology, and show that, in fact, people lie surprisingly little. We then formalize a wide range of potential explanations for the observed behavior, identify testable predictions that can distinguish between the models, and conduct new experiments to do so. Our empirical evidence suggests that a preference for being seen as honest and a preference for being honest are the main motivations for truth-telling.

We examine the reference-dependent risk preferences of Kőszegi and Rabin (2007), focusing on their choice-acclimating personal equilibria. Although their model has only a trivial intersection (expected utility) with other reference-dependent models, it has very strong connections with models that rely on different psychological intuitions. We prove that the intersection of rank-dependent utility and quadratic utility, two well-known generalizations of expected utility, is exactly monotone linear gain-loss choice-acclimating personal equilibria. We use these relationships to identify parameters of the model, discuss loss and risk aversion, and demonstrate new applications.

A long-standing puzzle is how overconfidence can persist in settings characterized by repeated feedback. This paper studies managers who participate repeatedly in a high-powered tournament incentive system, learning relative performance each time. Using reduced form and structural methods we find that (i) managers make overconfident predictions about future performance; (ii) managers have overly positive memories of past performance; (iii) the two phenomena are linked at an individual level. Our results are consistent with models of motivated beliefs in which individuals are motivated to distort memories of feedback and preserve unrealistic expectations.

People believe that, even in very large samples, proportions of binary signals might depart significantly from the population mean. We model this \"nonbelief in the Law of Large Numbers\" by assuming that a person believes that proportions in any given sample might be determined by a rate different than the true rate. In prediction, a nonbeliever expects the distribution of signals will have fat tails. In inference, a nonbeliever remains uncertain and influenced by priors even after observing an arbitrarily large sample. We explore implications for beliefs and behavior in a variety of economic settings.

During the 2010 gubernatorial elections, we elicit voter beliefs about the closeness of the election before and after showing different polls, which, depending on treatment, indicate a close or not-close race. Subjects update their beliefs in response to polls, but overestimate the probability of a very close election. However, turnout is unaffected by beliefs about election closeness. A follow-up RCT, conducted during the 2014 gubernatorial elections at much larger scale, also points to little relationship between poll information about closeness and turnout. We caveat that the strength of our evidence depends on assumptions regarding our treatments’ impacts on beliefs.

We extend well-known comparative results under expected utility to models of non-expected utility by providing novel conditions on local utility functions. We illustrate how our results parallel, and are distinct from, existing results for monotone comparative statics under expected utility, as well as risk preferences for non-expected utility. Our conditions generalize existing results for specific preferences (including expected utility) and allow us to verify monotone comparative statics for novel environments and preferences. We apply our results to portfolio choice problems where preferences or wealth might change, as well as precautionary savings.

Working Paper No. 23071 A common feature of many models of voter turnout is that increasing the perceived closeness of the election should increase voter turnout. However, cleanly testing this prediction is difficult and little is known about voter beliefs regarding the closeness of a given race. We conduct a field experiment during the 2010 US gubernatorial elections where we elicit voter beliefs about the closeness of the election before and after showing different polls, which, depending on treatment, indicate a close race or a not close race. We find that subjects update their beliefs in response to new information, but systematically overestimate the probability of a very close election. However, the decision to vote is unaffected by beliefs about the closeness of the election. A follow-up field experiment, conducted during the 2014 gubernatorial elections but at much larger scale, also points to little relationship between poll information about closeness and voter turnout.

Many models of economics assume that individuals distort objective probabilities. We propose a simple consistency condition on distortion functions, which we term distortion coherence, that ensures that the function commutes with conditioning on an event. We show that distortion coherence restricts belief distortions to have a particular function form: power-weighted distortions, where distorted beliefs are proportional to the original beliefs raised to a power and weighted by a state-specific value. We generalize our findings to allow for distortions of the probabilities assigned to both states and signals, which nests the functional forms widely used in studying probabilistic biases (e.g., Grether, 1980 and Benjamin, 2019). We show how coherent distorted beliefs are tightly related to several extant models of motivated beliefs: they are the outcome of maximizing anticipated expected utility subject to a generalized Kullback-Liebler cost of distortion. Moreover, in the domain of lottery choice, we link coherent distortions to explanations of non-expected utility like the Allais paradox: individuals who maximize subjective expected utility maximizers conditional on coherent distorted beliefs are equivalent to the weighted utility maximizers studied by Chew [1983].

This dissertation consists of three chapters on individual behavior in economic environments that feature search and learning. Chapter 1, ' Search and Temptation,' explores optimal search behavior when an agent experiences temptation regarding items previously observed during search. Although the agent's optimal policy is still a reservation policy, it has several novel features absent from other models of search. First, temptation has a threshold effect: the reservation value is an increasing function of the amount of temptation experienced. Second, temptation has a compromise effect: observing more tempting items means that the agent is no longer willing to choose items that are relatively low in temptation value, but is now willing to choose items relatively low in untempted utility. In Chapter 2, ' Revealed Search Theory,' a revealed preference approach is used to analyze models of search. Classical sequential search models characterize the value of the searcher's optimal policy as the solution to a Bellman equation. I define and provide behavioral foundations for a set of search models, which I refer to as Generalized Search Representations, that nests the classical model of sequential search, but also accommodates non-standard preferences. Chapter 3, 'A Model of Non-Belief in the Law of Large Numbers,' is joint with Daniel Benjamin and Matthew Rabin. Psychological research suggests that people believe that even in very large samples, proportions might depart significantly from the population mean. We model this \"non-belief in the Law of Large Numbers\" by assuming that a person believes that proportions in any given sample of binary signals might be determined by a rate different than the true rate. In inference, a non-believer attends too little to sample size, and remains uncertain even after observing an arbitrarily large sample. We explore the both the direct implications of non-belief, as well as how non-belief is often a necessary enabler of other biases, such as the over-influence of vivid signals, that would otherwise be overwhelmed by the logic of the Law of Large Numbers.

People believe that, even in very large samples, proportions of binary signals might depart significantly from the population mean.� We model this \"non-belief in the Law of Large Numbers\" by assuming that a person believes that proportions in any given sample might be determined by a rate different than the true rate.� In prediction, a non-believer expects the distribution of signals will have fat tails, more so for larger samples.� In inference, a non-believer remains uncertain and influenced by priors even after observing an arbitrarily large sample.� We explore implications for beliefs and behavior in a variety of economic settings.