• Article

We conducted a field experiment to study the effect of framing future moments in time as new beginnings (or “fresh starts”). University employees (N=6,082) received mailings with an opportunity to choose between increasing their contributions to a savings plan immediately or at a specified future time point. Framing the future time point in relation to a fresh start date (e.g., the recipient’s birthday, the first day of spring) increased the likelihood that the mailing recipient chose to increase contributions at that future time point without decreasing their likelihood of increasing contributions immediately. Overall, fresh start framing increased retirement plan contributions in the eight months following the mailing. Our findings represent the first experimental demonstration of the benefits of fresh start framing in a consequential field setting.

• 2021
• Working Paper

We develop a framework for assessing when a person will notice that a theory she has about the world is wrong, premised on the idea that people neglect information that they view (through the lens of their misconceptions) to be irrelevant. Focusing on the question of when a mistaken theory can persist in the long run even when attention is very cheap, we study the attentional stability of both general psychological biases---such as naivete about present bias or neglecting the redundancy in social information---and context-specific empirical misconceptions---such as false beliefs about medicinal side effects or financial investments. People discover their errors only when the data they deem relevant causes them to incidentally notice that their theory is wrong. We explore which combinations of errors and environments allow an error to persist. People tend to notice costly errors in a particular context when they are right about which factors matter even when they are wrong about how these factors matter, whereas errors that lead people to ubiquitously treat consequential factors as irrelevant will be stable across a broad class of environments. Environments designed to make such factors artificially germane can induce recognition of an error.

• 2021
• Working Paper

Why did the market rise yesterday? What are the implications of the latest school shooting? Why did a particular employee get promoted? To answer such questions, we often exchange models, stories, narratives, and interpretations with others. This paper provides a framework for thinking about such social exchanges of models. The key assumption, following Schwartzstein and Sunderam (2021), is that when people are exposed to multiple interpretations they adopt the one that best explains the data. A key implication is that within a network interpretations evolve. This evolution driven by social learning hardens reactions to data that are open to interpretation: following the exchange of models, people are more convinced they are able to explain the data. Thus, people in different networks can not only end up with vastly different beliefs, but in a sense be puzzled by the fact that others outside their network have different beliefs. For certain network structures, we show that social learning also mutes reactions to data that are open to interpretation: the exchange of models leaves beliefs closer to priors than they were before. Beyond studying fixed networks, we also consider how firm managers, politicians, and other agents are able to influence patterns of communication to their advantage. Agents who benefit from muting or broadly shared understandings will encourage a robust exchange of interpretations; agents who instead want new data to change behaviors will try to limit the exchange of interpretations, especially interpretations that suggest the data are not surprising. We apply the framework to consider the goal and structure of meetings in organizations, as well as the evolution and persistence of myths in social networks.