Martin Vaeth

Working Papers

Rational Voter Learning, Issue Alignment, and Polarization (SSRN) (new draft!)

2024 Best Job Market Paper Award (European Economic Association and UniCredit Foundation)

Abstract: We model electoral competition between two parties when voters rationally learn about their own political positions through flexible information acquisition. Rational learning polarizes voter preferences and aligns them across policy issues, even when true positions are unimodally distributed and independent. When parties strategically choose their positions to influence voter learning, party and voter polarization increase as information costs decline, and parties may adopt positions more extreme than their ideal policies. Endogenous learning introduces two forces on party positions: parties gain from moderating by biasing learning in their favor, and the more extreme party gains from polarizing by triggering more learning.

Attention and Regret (SSRN) - Revise and Resubmit at Journal of Political Economy

Abstract: This paper develops an optimality foundation for regret theory. We ask which emotional responses to decision outcomes would best incentivize an agent to pay more attention. Regret emerges as the uniquely optimal emotion. Our approach microfounds original regret theory (Bell 1982; Loomes and Sugden 1982) and enables its extension to account for decision complexity. Regret is stronger in simpler decision problems, consistent with a self-blame component. A scaling property allows extrapolation to new complexity environments without additional parameters.

Imprecision Attenuates Updating (arXiv) (new draft!)

Abstract: This paper studies how imprecision in noisy signals attenuates Bayesian posterior means toward the prior mean, a property underlying many comparative-statics results in information economics. We introduce the precision order, characterized by the attenuation effect across symmetric location experiments: noise density g̃ is more precise than g if and only if the posterior mean under g̃ is closer to the signal than under g for all signal realizations and all symmetric, log-concave priors. We apply the precision order to derive comparative statics for prior precision, the value of information, and voting.

The Optimal Design of Public Recognition Schemes

Abstract: We study the optimal design of public recognition schemes to incentivize agents who care about their social image – the public’s belief about their private type, such as ability or prosociality. We allow public recognition schemes to take the form of any signal structure, employing an information design approach. When agents are risk neutral over image, we show that one can restrict attention without loss to monotone partitional signals and characterize the optimum. If agents are risk averse over image, it may be optimal to maintain full privacy and not screen the agent – by contrast to monetary incentive schemes where screening remains optimal even under risk aversion.