Person:

Schwartzstein, Joshua

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.

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 that is 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 predicts 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.

Consumers suffer significant losses from not acting on available information. These losses stem from frictions such as search costs, switching costs, and rational inattention, as well as what we call mental gaps resulting from wrong priors/worldviews, or relevant features of a problem not being top of mind. Most research studying such losses does not empirically distinguish between these mechanisms. Instead, we show that most highly cited papers in this area presume one mechanism underlies consumer choices and assume away other potential explanations, or collapse many mechanisms together. We discuss the empirical difficulties that arise in distinguishing between different mechanisms, and some promising approaches for making progress in doing so. We also assess when it is more or less important for researchers to distinguish between these mechanisms. Approaches that seek to identify true value from demand, without specifying mechanisms behind this wedge, are most useful when researchers are interested in evaluating allocation policies that strongly steer consumers towards better options with regulation, traditional policy instruments, and defaults. On the other hand, understanding the precise mechanisms underlying consumer losses is essential to predicting the impact of mechanism policies aimed primarily at reducing specific frictions or mental gaps without otherwise steering consumers. We make the case that papers engaging with these questions empirically should be clear about whether their analyses distinguish between mechanisms behind poorly informed choices, and what that implies for the questions they can answer. We present examples from several empirical contexts to highlight these distinctions.

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 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.