Research

The Allais critique of expected utility theory (EUT) has led to the development of theories of choice under risk that relax the independence axiom but adhere to the fundamental/conventional axioms of ordering (completeness and transitivity) and monotonicity (with respect to first-order stochastic dominance). Unlike experimental work designed to test independence, our experiment is comprehensive—testing the entire set of axioms on which EUT is based. Our econometric analysis is also nonparametric and performed at the level of each individual subject. For the vast majority of subjects departures from independence are small relative to departures from ordering and/or monotonicity. [Appendix]

Accumulating evidence indicates that environmental temperature substantially affects economic outcomes and violence, but the reasons for this linkage are only partially understood. We study whether temperature directly influences behaviour by evaluating the effect of thermal stress on multiple dimensions of economic decision-making, judgement and destructive behaviour with 2,000 participants in Kenya and the United States who were randomly assigned to different temperatures in a laboratory. The main finding is that most major dimensions of economic decision-making are unaffected by temperature. We also find that heat significantly increases willingness to voluntarily destroy other participants’ assets in the Kenyan sample.

Ellsberg's famous paradox challenged Savage's subjective expected utility theory (EUT) -- which reduces uncertainty to risk -- by suggesting an aversion toward ambiguity. We provide a revealed preference test of the full set of axioms underpinning subjective EUT under uncertainty and compare it to an analogous test of objective EUT under risk. We find that individual choices are as consistent with utility maximization and expected utility maximization under uncertainty as they are under risk. Nevertheless, there is greater empirical scope for non-EUT models under uncertainty than under risk, and the absolute and relative consistency of EUT and non-EUT models vary considerably across subjects.

All models of economic decision-making assume that a person has a consistent set of preferences that governs their choice behavior. Whenever choices are in this way rational, there exists a utility function which, when maximized, rationalizes an individual's choice behavior. Revealed preference techniques can test if observed behavior is consistent with utility maximization, without imposing a particular utility functional form. Laboratory experiments generate sufficiently rich datasets for testing rationality, and the overarching results from past experiments is that choices are sufficiently consistent to be considered rational. However, questions of external validity remain -- are subjects utility maximizers due to the laboratory setting itself, and short time span of the experiment? To answer this, we replicate the laboratory computer interface on a custom application which subjects download onto their smartphones. Subjects make choices throughout the day over a period of 5 days, while going about their lives, instead of over one hour in the laboratory. We find that scores on standard measures of rationality are as high among subjects in the mobile experiment as those in the lab, and that most are close to the ideal of perfectly rational behavior. Our study provides temporal and environmental external validation for the laboratory results, as well as support for rational behavior in other lab-in-field smartphone experiments.

The assumption of rationality is used to infer preferences from observed choices, but classic economic theory provides scant guidance when choice data has error. Methods to estimate preferences from noisy data inevitably invoke additional assumptions on those very preferences. This paper presents a procedure to detect and measure error in an individual's observed choice data when the individual has an underlying rational choice process that has been contaminated with random implementation errors. Using a single individual's choices over many menus, I construct an observed revealed preference relation, and prove it is a random graph whose acyclicity is equivalent to rationality. Exploiting the structure in the graph produced by the contaminating errors, I devise a classifier able to detect which observations are errors and an estimator to measure the rate at which errors occur. These two methods can be applied to any dataset in which an individual makes constrained choices from a sequence of overlapping non-identical menus, regardless of the choice environment. I apply the method to a benchmark dataset of choices observed in the lab (Choi et al. 2007) and show that most individuals have error rates between 5% and 14.5% (interquartile range). I show that three existing measures of goodness-of-fit for rationality, which are often used as proxies for error estimates, are not robust, not identified, or biased when choices are observed with error.