We present a framework where “model persuaders” influence receivers’ beliefs by proposing models that organize past data to make predictions. Receivers are assumed to find models more compelling when they better explain the data, fixing receivers’ prior beliefs. Model persuaders face a trade-off: better-fitting models induce less movement in receivers’ beliefs. Consequently, a receiver exposed to the true model can be most misled by persuasion when that model fits poorly, competition between persuaders tends to neutralize the data by pushing toward better-fitting models, and a persuader facing multiple receivers is more effective when he can send tailored, private messages.

Fixed differences loom smaller when compared to large differences. We propose a model of relative thinking where a person weighs a given change along a consumption dimension by less when it is compared to bigger changes along that dimension. In deterministic settings, the model predicts context effects such as the attraction effect but predicts meaningful bounds on such effects driven by the intrinsic utility for the choices. In risky environments, a person is less likely to sacrifice utility on one dimension to gain utility on another that is made riskier. For example, a person is less likely to exert effort for a fixed monetary return if there is greater overall income uncertainty. We design and run experiments to test basic model predictions and find support for these predictions.

We present a model of uninformative persuasion in which individuals "think coarsely": they group situations into categories and apply the same model of inference to all situations within a category. Coarse thinking exhibits two features that persuaders take advantage of: (i) transference, whereby individuals transfer the informational content of a given message from situations in a category where it is useful to those where it is not, and (ii) framing, whereby objectively useless information influences individuals' choice of category. The model sheds light on uninformative advertising and product branding, as well as on some otherwise anomalous evidence on mutual fund advertising.

A fundamental implication of standard moral hazard models is overuse of low-value medical care because copays are lower than costs. In these models, the demand curve alone can be used to make welfare statements, a fact relied on by much empirical work. There is ample evidence, though, that people misuse care for a different reason: mistakes, or "behavioral hazard." Much high-value care is underused even when patient costs are low, and some useless care is bought even when patients face the full cost. In the presence of behavioral hazard, welfare calculations using only the demand curve can be off by orders of magnitude or even be the wrong sign. We derive optimal copay formulas that incorporate both moral and behavioral hazard, providing a theoretical foundation for value-based insurance design and a way to interpret behavioral "nudges." Once behavioral hazard is taken into account, health insurance can do more than just provide financial protection - it can also improve health care efficiency.

We consider a model of technological learning under which people “learn through noticing”: they choose which input dimensions to attend to and subsequently learn about from available data. Using this model, we show how people with a great deal of experience may persistently be off the production frontier because they fail to notice important features of the data they possess. We also develop predictions on when these learning failures are likely to occur, as well as on the types of interventions that can help people learn. We test the model’s predictions in a field experiment with seaweed farmers. The survey data reveal that these farmers do not attend to pod size, a particular input dimension. Experimental trials suggest that farmers are particularly far from optimizing this dimension. Furthermore, consistent with the model, we find that simply having access to the experimental data does not induce learning. Instead, behavioral changes occur only after the farmers are presented with summaries that highlight previously unattended-to relationships in the data.

People make personal plans regarding whether, when, where, and how to undertake certain actions. We discuss three questions related to personal plans. First, what are the effects of plans on behavior? Second, when are plans formed? Third, how do plans deviate from optimality? For each of these questions, we (a) offer a brief overview of research that sheds light on the issue and (b) identify gaps in current knowledge. We emphasize connections to the growing theoretical literature that gives personal plans a substantive role, but we conclude that more research is needed, especially on the latter two questions we cover.

This article examines the consequences and causes of low enrollment of Black patients in clinical trials. We develop a simple model of similarity-based extrapolation that predicts that evidence is more relevant for decision-making by physicians and patients when it is more representative of the group being treated. This generates the key result that the perceived benefit of a medicine for a group depends not only on the average benefit from a trial but also on the share of patients from that group who were enrolled in the trial. In survey experiments, we find that physicians who care for Black patients are more willing to prescribe drugs tested in representative samples, an effect substantial enough to close observed gaps in the prescribing rates of new medicines. Black patients update more on drug efficacy when the sample that the drug is tested on is more representative, reducing Black-white patient gaps in beliefs about whether the drug will work as described. Despite these benefits of representative data, our framework and evidence suggest that those who have benefited more from past medical breakthroughs are less costly to enroll in the present, leading to persistence in who is represented in the evidence base.