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Apparently mistaken decisions are ubiquitous. To what extent does this reflect irrationality, as opposed to a rational trade-off between the costs of information acquisition and the expected benefits of learning? We develop a revealed preference test that characterizes all patterns of choice \"mistakes\" consistent with a general model of optimal costly information acquisition and identify the extent to which information costs can be recovered from choice data.

We provide testable conditions under which the cost of acquiring information is given by the expected reduction of a measure of uncertainty (e.g., entropy). The assumption, under the name of posterior separability, is nearly universal in the literature of rational inattention; yet, a testable characterization has been lacking. In applications to experimental data, we indicate situations in which posterior separability is—and is not—a compelling assumption for the cost of information; we propose a generalization to address some of its shortcomings. We also show how to identify and estimate nonparametrically the cost of information from observable choice behavior.

Recommender systems are an integral part of the online retail environment. Prior research has focused largely on computational approaches to improving recommendation accuracy, and only recently researchers have started to study their behavioral implications and potential side effects. We used three controlled experiments, in the context of purchasing digital songs, to explore the willingness-to-pay judgments of individual consumers after being shown personalized recommendations. In Study 1, we found strong evidence that randomly assigned song recommendations affected participants’ willingness to pay, even when controlling for participants’ preferences and demographics. In Study 2, participants viewed actual system-generated recommendations that were intentionally perturbed (introducing recommendation error), and we observed similar effects. In Study 3, we showed that the influence of personalized recommendations on willingness-to-pay judgments was obtained even when preference uncertainty was reduced through immediate and mandatory song sampling prior to pricing. The results demonstrate the existence of important economic side effects of personalized recommender systems and inform our understanding of how system recommendations can influence our everyday preference judgments. The findings have significant implications for the design and application of recommender systems as well as for online retail practices. The online appendix is available at https://doi.org/10.1287/isre.2017.0703 .

The presence of hypothetical bias (HB) associated with stated preference methods has garnered frequent attention in the broad literature trying to describe and understand human behavior, often seen in environmental valuation, marketing studies, transportation choices, medical research, and others. This study presents an updated meta-analysis to explore the source of HB and methods to mitigate it. While previous meta-analysis on this topic often involves a few dozen articles, this analysis includes 131 studies after reviewing over 500 published and unpublished articles. This enables the inclusion of several important factors that have not been investigated before. These include relatively recent willingness to pay elicitation methods such as choice experiments and the Turnbull lower bound estimator. Newly emerged HB reduction techniques such as consequentiality and certainty follow-up treatments are also included. For explanatory variables that have been examined in previous studies, this analysis does not always report consistent findings. In particular, holding everything constant and contrary to commonlyheld beliefs, the method of auction does not offer much reduction to HB compared to more conventional methods such as a referendum vote. However, choice experiment, cheap talk, consequentiality and certainty follow-up all significantly contributed to explaining and mitigating the magnitude of HB. These results help practitioners to understand HB’s presence and choose appropriate methods for amelioration. The framework established through this study also enables future analyses targeted at understanding variations built upon one or multiple HB mitigation techniques.

We assess the empirical importance of changes in income and relative prices for structural transformation in the postwar United States. We explain two natural approaches to the data: sectors may be categories of final expenditure or value added; e.g., the service sector may be the final expenditure on services or the value added from service industries. We estimate preferences for each approach and find that with final expenditure income effects are the dominant force behind structural transformation, whereas with value-added categories price effects are more important. We show how the inputoutput structure of the United States can reconcile these findings.

This study explores the tension between the standard economic theory of preference and nonstandard theories of preference that are motivated by an underlying theory of framing. A simple experiment fails to measure a known preference. The divergence of the measured preference from the known preference reflects a mistake, arising from some subjects’ misconception of the game form. We conclude that choice data should not be granted an unqualified interpretation of preference revelation. Mistakes in choices obscured by a possible error at the foundation of the theory of framing can masquerade as having been produced by nonstandard preferences.

We model a boundedly rational agent who suffers from limited attention. The agent considers each feasible alternative with a given (unobservable) probability, the attention parameter, and then chooses the alternative that maximizes a preference relation within the set of considered alternatives. We show that this random choice rule is the only one for which the impact of removing an alternative on the choice probability of any other alternative is asymmetric and menu independent. Both the preference relation and the attention parameters are identified uniquely by stochastic choice data.

Revealed preference theory is brought to bear on the problem of recovering approximate parametric preferences from consistent and inconsistent consumer choices. We propose measures of the incompatibility between the revealed preference ranking implied by choices and the ranking induced by the considered parametric preferences. These incompatibility measures are proven to characterize well-known inconsistency indices. We advocate a recovery approach that is based on such incompatibility measures and demonstrate its applicability for misspecification measurement and model selection. Using an innovative experimental design, we empirically substantiate that the proposed revealed-preference-based method predicts choices significantly better than a standard distance-based method.

This paper proposes foundations and a methodology for survey-based tracking of well-being. First, we develop a theory in which utility depends on \"fundamental aspects\" of well-being, measurable with surveys. Second, drawing from psychologists, philosophers, and economists, we compile a comprehensive list of such aspects. Third, we demonstrate our proposed method for estimating the aspects' relative marginal utilities—a necessary input for constructing an individual-level well-being index—by asking ~4,600 US survey respondents to state their preference between pairs of aspect bundles. We estimate high relative marginal utilities for aspects related to family, health, security, values, freedom, happiness, and life satisfaction.

Revealed preference theory offers a criterion for decision-making quality: if decisions are high quality then there exists a utility function the choices maximize. We conduct a large-scale experiment to test for consistency with utility maximization. Consistency scores vary markedly within and across socioeconomic groups. In particular, consistency is strongly related to wealth: A standard deviation increase in consistency is associated with 15-19 percent more household wealth. This association is quantitatively robust to conditioning on correlates of unobserved constraints, preferences, and beliefs. Consistency with utility maximization under laboratory conditions thus captures decision-making ability that applies across domains and influences important real-world outcomes.