Kailin Chen

Research

1.  Learning from Strategic Sources 

Extended abstract in EC’24. 

This paper studies learning from multiple informed agents where each agent has a small piece of information about the unknown state of the world in the form of a noisy signal and sends a message to the principal, who then makes a decision that is not constrained by predetermined rules. In contrast to the existing literature, I model the conflict of interest between the principal and the agents more generally and consider the case where the preferences of the principal and the agents are misaligned in some realized states. I show that if the conflict of interest between the principal and the agents is moderate, there is a discontinuity: when the number of agents is large enough, adding even a tiny probability of misaligned states leads to complete unraveling in which the agents ignore their signals, in contrast to the almost complete revealing that is predicted by the existing literature. Furthermore, I demonstrate that no matter how small the conflict between the principal and the agents is, the information contained in each agent's message must vanish as the number of agents grows large. Finally, no matter how many agents there are, the total amount of information that is transmitted is limited, and the principal always fails to fully learn the unknown state.

2.  Information Aggregation in Collective Experimentation

This paper considers a dynamic voting model in which voters repeatedly make a collective decision about whether to experiment with an unknown reform or stay with the status quo. Experimenting with the reform brings each voter new information about whether she benefits from it, and hence generates dispersed information among the voters about the reform's overall suitability. We examine how strategic voting shapes the voters' incentives for experimentation, and whether elections can aggregate and utilize the voters' dispersed information. We show that, when the number of voters is large, requiring a higher voting threshold for the reform leads to increased experimentation. Furthermore, we demonstrate that information is aggregated only if the voting threshold required for the reform is sufficiently low.

3.  Fishing for Approval Supplementary Appendix (with Stephan Lauermann and  Mehmet Ekmekci)

There is a concern that a biased agent might fish for approval if one assent undoes all past rejections. In this paper, we study a sequential voting model in which a biased organizer engages in a costly search to solicit one vote for his preferred policy. Voters have common, state-dependent preferences. The organizer is informed about the realized state while the voters obtain noisy information via private signals. We show that, somewhat paradoxically, rather than hurting, the organizer's ability to fish for approval helps the voters, often leading to the voters' first-best, full-information equivalent outcome.

4. Experiments in the Linear Convex Order. arXiv

This paper proposes two rankings of statistical experiments based on the linear convex order, providing simpler and more tractable characterizations than Blackwell order, which relies on the convex order. We apply these rankings to compare statistical experiments in decision problems with a binary-action space and in decision problems that aggregate payoffs over a collection of binary-action decision problems. Furthermore, these rankings can be used to compare statistical experiments in moral hazard problems without requiring the first-order approach to be valid, thereby complementing the results in Holmström (1979) and Kim (1995).