• September 2023
• Article

Reputation is a powerful driver of human behavior. Reputation systems incentivize 'actors' to take reputation-enhancing actions, and 'evaluators' to reward actors with positive reputations by preferentially cooperating with them. This article proposes a reputation framework that centers the perspective of evaluators by suggesting that reputation systems can create two fundamentally different incentives for evaluators to reward positive reputations. Evaluators may be pulled towards 'good' actors to benefit directly from their reciprocal cooperation, or pushed to cooperate with such actors by normative pressure. I discuss how psychology and behavior might diverge under pull versus push mechanisms, and use this framework to deepen our understanding of the empirical reputation literature and suggest ways that we may better leverage reputation for social good.

• 2023
• Working Paper

We document two new facts about the distributions of answers in famous statistical problems: they are i) multi-modal and ii) unstable with respect to irrelevant changes in the problem. We offer a model in which, when solving a problem, people represent each hypothesis by attending “bottom up” to its salient features while neglecting other, potentially more relevant, ones. Only the statistics associated with salient features are used, others are neglected. 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 about how changes in the salience of specific features should jointly shape the prevalence of these biases and measured attention to features, but also create entirely new biases. We test and confirm these predictions experimentally. Bottom-up attention to features emerges as a unifying framework for biases conventionally explained using a variety of stable heuristics or distortions of the Bayes rule.

• 2023
• Working Paper

We develop a framework for assessing when somebody will eventually notice that she has a misspecified model of the world, premised on the idea that she neglects information that she deems—through the lens of her misconceptions—to be irrelevant. In doing so, we assess the attentional stability of both general psychological biases—such as naivete about present bias—and empirical misconceptions—such as false beliefs about consumer demand. We explore which combinations of errors and environments allow an error to persist, versus which errors lead people to incidentally learn they have things wrong because even the data they deem relevant tells them that something is amiss. We use the framework to shed light on why fresh eyes are valuable in organizational problems, why people persistently use overly coarse (vs. overly fine) categorizations, why people sometimes recognize their errors in complex environments when they don’t in simple environments, and why people recognize errors in others that they don’t recognize in themselves.