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. (JEL D11, D81)

We introduce a flexible framework to study probabilistic choice that accommodates heterogeneous types and bounded rationality. We provide a novel progressive structure for the heterogeneous types to capture heterogeneity due to varying levels of a behavioral trait. Given an order of alternatives, our progressive structure sorts the types by the extent to which they align with this order. Unlike the random-utility model, our model uniquely identifies the heterogeneity, allowing policy makers to perform an improved welfare analysis. As a showcase, we provide characterization of a well-studied type of bounded rationality: “less-is-more.” In addition, we provide conditions for unique identification of the underlying order for the less-is-more structure.

We examine whether people have an intrinsic preference for negatively skewed or positively skewed information structures and how these preferences relate to intrinsic preferences for informativeness. The results from lab experiments show a strong intrinsic preference for positively skewed information and suggest that providing such information can improve information uptake. Evidence from field studies in decision- and ego-relevant contexts replicates these findings. We discuss our findings through the lens of existing theories and the potential trade-offs in information provision policies. (JEL C91, C93, D12, D82, D83)

Abstract We propose and axiomatize the categorical thinking model (CTM) in which the framing of the decision problem affects how agents categorize alternatives, that in turn affects their evaluation of it. Prominent models of salience, status quo bias, loss-aversion, inequality aversion, and present bias all fit under the umbrella of CTM. This suggests categorization is an underlying mechanism of key departures from the neoclassical model of choice. We specialize CTM to provide a behavioural foundation for the salient thinking model of Bordalo et al. (2013, Journal of Political Economy, 121, 803–843) that highlights its strong predictions and distinctions from other models.

The standard revealed preference argument relies on an implicit assumption that a decision maker considers all feasible alternatives. The marketing and psychology literatures, however, provide well-established evidence that consumers do not consider all brands in a given market before making a purchase (Limited Attention). In this paper, we illustrate how one can deduce both the decision maker's preference and the alternatives to which she pays attention and inattention from the observed behavior. We illustrate how seemingly compelling welfare judgments without specifying the underlying choice procedure are misleading. Further, we provide a choice theoretical foundation for maximizing a single preference relation under limited attention. (JEL D11, D81)

We introduce a new model of stochastic choice that assigns each choice option a utility, along with a salience parameter reflecting economic frictions. We characterize our model behaviorally and investigate its comparative statics properties. We show that the model generates intuitive closed-form solutions in equilibrium settings where firms can choose price, quality, and advertising. In addition, we show that the model allows for flexible substitution patterns and changes in market shares across choice sets. We demonstrate that our model can be easily identified and can outperform alternatives in demand prediction. (JEL D11, D21, D43, M37)

This paper illustrates how one can deduce preference from observed choices when attention is both limited and random. We introduce a random attention model where we abstain from any particular attention formation and instead consider a large class of nonparametric random attention rules. Our intuitive condition, monotonic attention, captures the idea that each consideration set competes for the decision maker’s attention. We then develop a revealed preference theory and obtain testable implications. We propose econometric methods for identification, estimation, and inference for the revealed preferences. Finally, we provide a general-purpose software implementation of our estimation and inference results and simulation evidence.