Misperceived Social Norms: A Unified Framework (Job Market Paper)
Received the 2026 EAYE Best Paper Award
Perceptions of others are central to behavior under social influence, yet they are often inaccurate. This paper develops a unified framework to study how various kinds of misperceptions about others impact collective outcomes when individuals are subject to distinct types of social influence. In particular, I distinguish between two types of social influence: conformity to others' actions and conformity to others' attitudes. Although often treated interchangeably, I find that misperceptions have sharply different implications across the two: under conformity only to actions, misperceptions about others' attitudes or individual actions cannot shift collective behavior, whereas under conformity to attitudes, such misperceptions can significantly impact collective outcomes. I show that these two types of social influence also yield distinct policy and welfare implications. I discuss diverse applications of my framework, including norm-based interventions, the gender gap in competitive entry and the "friendship paradox" in networks.
Previous title: A Theory of Misperceived Social Preferences and Norms
Preferences for Information Bias with Anticipated Learning (joint with Paul O. Richter)
Individuals are frequently exposed to information they do not actively seek, such as news shared by others. This paper studies how such anticipated information shapes agents’ learning decisions. We show that agents’ preferences for biased sources depend systematically on their prior beliefs and the structure of the anticipated information. We identify two novel patterns, matching and mismatching, in which agents select sources biased towards the same or the opposite action compared to the information they expect to receive. In social environments, when agents share information with their peers, the model predicts that groups of relatively uncertain people tend to coordinate on consuming news with the same bias, especially if the degree of bias is large. In other cases, they might coordinate on choosing information with diverse biases.
Previous title: Forecasted learning
Emotional Feedback Design: Theory and Experiment (joint with Eva Spantidaki-Kyriazi)
Received the REACT Grant by Universitat Pompeu Fabra
Firms, schools, and other institutions choose how precisely to measure and report individual performance, even when material stakes are unaffected. We study whether this choice matters by varying the anticipated informativeness of performance feedback in a between-subjects lab experiment, holding material incentives fixed. Participants complete two ego-relevant tasks, a timed arithmetic task and a pattern-recognition task, expecting either highly informative or noisier feedback about their performance. Under highly informative feedback, participants perform significantly worse in the arithmetic task, where time pressure is severe, and show a similar pattern in the pattern-recognition task among those for whom time is binding. This gap is not explained by lower effort: behavioral and self-reported measures show no treatment difference in effort, while informative feedback reduces the efficiency with which effort converts into performance. These results identify self-image concerns as a channel through which the informativeness of feedback systems , not just their content, can shape performance.