Games and Economic Behavior, Vol. 142, Pages 315-338, 2023
This paper studies the long-term interaction between two overconfident agents who choose how much effort to exert while learning about their environment. Overconfidence causes agents to underestimate either a common fundamental, such as the underlying quality of their project, or their counterpart’s ability, to justify their worse-than-expected performance. We show that in many settings, agents create informational externalities for each other. When informational externalities are positive, the agents’ learning processes are mutually-reinforcing: one agent best responding to his own overconfidence causes the other agent to reach a more distorted belief and take more extreme actions, generating a positive feedback loop. The opposite pattern, mutually-limiting learning, arises when informational externalities are negative. We also show that in our multi-agent environment, overconfidence can lead to Pareto improvement in welfare. Finally, we prove that under certain conditions, agents’ beliefs and effort choices converge to a steady state that is a Berk-Nash equilibrium.

Revise & resubmit at the Quarterly Journal of Economics
This paper explores how cognitive constraints–namely, attention and processing capacity–interact with properties of the learning environment to determine how people react to information. In our model, people form a simplified mental representation of the environment via salience-channeled attention, then process information with cognitive imprecision. The model predicts overreaction to information when environments are complex, signals are noisy, information is surprising, or priors are concentrated on less salient states; it predicts underreaction when environments are simple, signals are precise, information is expected, or priors are concentrated on salient states. Results from a series of pre-registered experiments provide support for these predictions and direct evidence for the proposed cognitive mechanisms. We show that the two psychological mechanisms act as cognitive complements: their interaction is critical for explaining belief data and together they yield a highly complete model in terms of capturing explainable variation in belief-updating. Our theoretical and empirical results connect disparate findings in prior work: underreaction is typically found in laboratory studies, which feature simple learning settings, while overreaction is more prevalent in financial markets which feature greater complexity.

Revise & resubmit at the American Economic Review
This paper studies which misspecified models are likely to persist when decision-makers compare them with competing models. The main result provides a characterization of such models based on two features that are straightforward to derive from the primitives: the model’s asymptotic accuracy in predicting the equilibrium pattern of observed outcomes and the tightness of the prior around such equilibria. Misspecified models can be robust, persisting against a wide range of competing models—including the correct model—despite individuals observing an infinite amount of data. Moreover, simple misspecified models equipped with entrenched priors can be more robust than complex correctly specified models.

Social media influencers often endorse products under brand sponsorships without disclosing the nature of their relationship with the brand. This paper explores the incentives for truth-telling when influencers face private sponsorship opportunities and the potential consequences for consumers. While reputation concerns can encourage influencers to be truthful when promoting sponsored products, they may also lead to under-endorsement when no sponsorship opportunity exists. As sponsorship opportunities become more abundant, the quality of information transmission initially decreases before increasing. Therefore, new technologies that efficiently match influencers with sponsors may improve consumer welfare. The Federal Trade Commission’s mandatory disclosure rules are also shown to benefit consumers without necessarily harming influencers.