Kailin Chen


Hi, I am a post-doctoral researcher at the Department of Economics, Aalto University School of Business. 

My main interests are in microeconomic theory, particularly voting and learning.

You can find my cv here. 

Email: kailin.chen@aalto.fi

Research

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.


Implementing a reform has divergent effects on a population and generates dispersed information concerning its overall suitability. This paper analyzes a collective experimentation model in which voters gradually learn their payoffs, which are divergent among them. Furthermore, their payoffs depend on the unknown state of the world. Hence, experimentation generates information concerning the unknown state, which is dispersed among the voters. We are interested in how strategic voting shapes incentives for experimentation, and more importantly, whether elections can aggregate and utilize the voters' private information concerning the unknown state. We show that a stricter rule for experimentation leads to more experimentation when the number of voters is large, and demonstrate that information is effectively aggregated only if the voting rule is biased toward experimentation.


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.