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.

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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. *

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. 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.

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. 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.