We introduce a novel measure to quantify deviations from Bayesian updating that accounts for the informativeness of evidence. It is defined as the minimum adjustment to a (potentially unobserved) prior required to rationalize observed beliefs as Bayesian. The measure has a simple closed-form expression and admits an alternative interpretation as the maximum expected monetary payoff that can be extracted from non-Bayesian behavior. It responds intuitively to parameters in well-known models of non-Bayesian updating and provides a cardinal tool for comparing belief-updating behavior across individuals, treatments, and populations. Applying the measure to a pooled sample of 34,569 belief reports from six experimental studies, we uncover novel patterns in updating behavior, including that individuals behave closer to Bayes' rule when they overreact than when they underreact to information, and that deviations from Bayesian updating are larger following information that confirms prior beliefs.

We model a boundedly rational agent who exhibits limited and heterogeneous attention. The agents' attention can be naturally sorted in an ascending order. We provide a characterization result using two simple behavioral postulates. Unlike earlier models, consideration sets' composition and frequency are uniquely identified. Within our framework, we accommodate unobserved heterogeneity in attention and unobserved preferences. Furthermore, we explore various extensions of our model and provide insights into the comparative statics of attentiveness levels in two probabilistic choices.

This paper studies choice situations in which a decision maker can choose multiple alternatives. Given a menu of available options, the decision maker selects a subset of the menu with certain probabilities. We employ an axiomatic approach to characterize various parametric models in the literature. Our results elucidate the implications of the functional form assumptions and shed light on the distinctions between models. The behavioral postulates offer simple tools for testing and falsifying the choice procedures used by the decision maker and reveal a close connection between models that are seemingly unrelated.

We investigate preferences for acting stochastically in the context of social preferences. We analyze a Probabilistic Dictator Game under a broad class of models that incorporate ex-ante and ex-post social preferences as well as stochastic choice models that motivate stochastic giving beyond pro-social reasons and study their implications with lab experiments. The behavior of a majority of dictators is inconsistent with the models of ex-ante/ex-post social preferences. Nevertheless, a majority of dictators are consistent with the axioms of stochastic transitivity and regularity. Furthermore, their behaviors are in line with an Additive Perturbed Utility model.

This paper introduces the Dual-System Thinking (DST) model, a decisiontheoretic framework that integrates psychological dual-process theories into economic modeling. A single cognitive weight parameter governs the relative influence of the automatic and deliberate cognitive systems. Even the simplest form of DST exhibits distinct behavioral patterns, suggesting that the psychological insights of dual-system theory offer a distinct and valuable approach to modeling choice behavior. Empirically, we show that the model can accommodate several empirical findings and outperform well-known models in discrete choice analysis across various contexts. We also apply the model to study optimal list design and rationality in stochastic environments.

This paper studies how noise in certification technology affects seller profits in a duopoly with unobservable product quality. We identify two opposing effects of noisy certification. First, it reduces the informativeness of certification outcomes, homogenizing buyers' beliefs and limiting the scope for vertical differentiation. Second, it introduces randomness into buyer perceptions, endogenously generating differentiation between otherwise similar products. When buyers are risk-neutral, the first effect dominates, reducing seller profits. However, when buyers are loss averse, the negative impact of reduced informativeness is mitigated, and noisy certification can increase profits relative to accurate certification. Experimentally, treatments with inaccurate certification are more profitable than those with accurate certification, particularly in settings with intense competition.