2026 Theses Doctoral

Information and Incentives under Uncertainty

Zhou, Yangfan

This dissertation comprises three papers that develop and apply theoretical tools to study the incentives and strategic behavior of economic agents under uncertainty. The first chapter examines the design of mechanisms to address incentive problems arising from private information under ambiguity, while the remaining two chapters explore how information (acquisition and transmission) shapes agents' strategic behavior.

In Chapter 1, Knowledge-Based Mechanisms (co-authored with Yutong Zhang), we study robust mechanisms when the designer possesses a Bayesian belief over some components of agents' private information but faces ambiguity over others. The designer evaluates mechanisms by their worst-case performance over all joint distributions consistent with her belief over the Bayesian components. The framework encompasses settings such as multidimensional delegation in which a principal knows the distribution of the state but not the agent's preferences (e.g., his trade-offs across dimensions), screening in which a seller only knows certain quantiles of the buyer's value distribution or only has misspecified estimates of buyer preferences, and auction design or social choice when agents' beliefs about each other are ambiguous to the designer. We provide sufficient conditions under which a knowledge-based mechanism---one that conditions only on the Bayesian components but not the ambiguous ones---is robustly optimal. Our results unify earlier work across distinct economic environments and uncover new applications.

In Chapter 2, Collective Sampling, I study collective dynamic information acquisition. Players decide when to stop sequential sampling via a collective stopping rule, which specifies decisive coalitions that can terminate information acquisition upon agreement. I develop a methodology to characterize equilibria using an ex ante perspective. Instead of stopping strategies, players choose distributions over posterior beliefs subject to majorization constraints. Equilibrium sampling regions are characterized via a fixed-point argument based on concavification. Collective sampling generates learning inefficiencies and having more decisive coalitions typically reduces learning. I apply the model to committee search and competition in persuasion.

In Chapter 3, Pitfall of Precision in Noisy Signaling (co-authored with Shuhua Si), I study a model where a principal decides whether to approve an agent based on noisy signals (e.g., test scores) generated by the agent. High-quality agents can produce high signals on average at lower cost, but the realizations are subject to noise that depends on the screening technology's precision. We uncover a paradoxical ''pitfall of precision'': when precision is already high, further improvements reduce screening accuracy and lower the principal's welfare. This occurs because greater precision incentivizes strategic signaling from more low-quality agents, outweighing the direct benefit from improved precision. We also examine how commitment helps mitigate this pitfall.

A recurring theme of this dissertation is that more information, more flexibility, or greater precision need not improve outcomes once incentives are taken seriously. Information is not merely an input into decision-making; it is also an object of strategic response, interaction, and institutional design. Through the study of robust mechanism design, collective learning, and noisy signaling, this dissertation illuminates how information and incentives jointly shape economic outcomes, and why improving institutions often requires understanding not only what agents know, but also how they behave in response to what can be known.