Abstract: We present a theory of choice in which attention to the features of options is determined by the decision maker’s categorization of the current problem in a set of problems she solved in the past. Categorization depends on goal-relevant and contextual problem-level features. The model yields heterogeneity in attention and choice in a given problem based on different past experiences, choice rigidity when categorization does not change despite new data, and choice instability when changes in irrelevant context cause re-categorization. We show that heterogeneous and unstable mental representations unify major biases in judgments and decision making.

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Abstract: We embed diagnostic expectations in a workhorse neoclassical model with heteroge- neous firms and risky debt. A realistic degree of overreaction estimated from US firms' earnings forecasts generates realistic credit cycles. Good times produce economic and financial fragility, predicting future disappointment of expectations, low bond returns, and investment declines. To generate the size of spread increases observed during 2007-9, the model requires only moderate negative shocks. Diagnostic expectations offer a realistic, parsimonious way to produce financial reversals in business cycle models.

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Abstract: For standard statistical problems, we provide new evidence documenting i) multi-modality and ii) instability in probability estimates, including from irrelevant changes in problem description. The evidence motivates a model in which, when solving a problem, people represent each hypothesis by attending to its salient features while neglecting other, potentially more relevant, ones. Only the statistics associated with salient features are used. The model unifies biases in judgments about i.i.d. draws, such as the Gambler's Fallacy and insensitivity to sample size, with biases in inference such as under- and overreaction and insensitivity to the weight of evidence. The model makes predictions for how changes in the salience of specific features jointly shapes known biases and measured attention to features, but also create entirely new biases. We test and confirm these predictions experimentally. Salience-driven attention to features emerges as a unifying framework for biases conventionally explained using a variety of stable heuristics or distortions of the Bayes rule.

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Abstract: How do people form beliefs about novel risks, with which they have little or no experience? Motivated by survey data on beliefs about Covid we collected in 2020, we build a model based on the psychology of selective memory. When a person thinks about an event, different experiences compete for retrieval, and retrieved experiences are used to simulate the event based on how similar they are to it. The model predicts that different experiences interfere with each other in recall and that non domain-specific experiences can bias beliefs based on their similarity to the assessed event. We test these predictions using data from our Covid survey and from a primed-recall experiment about cyberattack risk. In line with our theory of similarity-based retrieval and simulation, experiences and their measured similarity to the cued event help account for experience effects, priming effects, and the interaction of the two in shaping beliefs.

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Abstract: We address the joint hypothesis problem in cross-sectional asset pricing by using measured analyst expectations of earnings growth. We construct a firm-level measure of Expectations Based Returns (EBRs) that uses analyst forecast errors and revisions and shuts down any cross-sectional differences in required returns. We obtain three results. First, variation in EBRs accounts for a large chunk of cross-sectional return spreads in value, investment, size, and momentum factors. Second, time variation in these spreads is predictable, and proxied by predictable time variation in EBRs. This result holds even controlling for scaled price variables, which may capture time varying required return differentials. Third, firm characteristics typically viewed as capturing risk predict disappointment of expectations (and of EBRs). Overall, return spreads typically attributed to exotic risk factors are explained by predictable movements in non-rational expectations of firms’ earnings growth.

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