Kailin Chen

Research

1.  Communication with Multiple Senders. arXiv

Previously circulated under the title Learning from Strategic Sources.

Extended abstract in EC’24. 

This paper analyzes a cheap talk model with one receiver and multiple senders. Each sender observes a noisy signal regarding an unknown state of the world. Existing literature (e.g., Levit and Malenko, 2011; Battaglini, 2017) focuses on scenarios where the receiver and senders have aligned preferences in each state. We further explore situations with disagreement states in which the receiver and the senders have misaligned preferences. We first show that, when the number of senders grows large, each sender's message must convey almost no information to the receiver. Furthermore, we identify a discontinuity in information transmission: with moderate conflict between the receiver and the senders, introducing an arbitrarily small probability of disagreement states causes complete unraveling, contrary to full learning predicted by the literature. Finally, we demonstrate that the receiver cannot fully learn the state even when receiving messages from arbitrarily many senders.

2. Experiments in the Linear Convex Order. arXiv

Extended abstract in EC’25. 

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).

3.  Collective Experimentation with Correlated Payoffs

Revise & Resubmit, Theoretical Economics. 

This paper studies an exponential bandit model in which a group of agents collectively decide whether to undertake a risky action. This action is implemented if the fraction of agents voting for it exceeds a predetermined threshold. Building on Strulovici (2010), which assumes the agents' payoffs are independent, we explore the case in which the agents' payoffs are correlated. During experimentation, each agent learns individually whether she benefits from the risky action; in this way, she also gains information about its overall desirability. Furthermore, each agent is able to learn indirectly from the others, because in making her decisions, she conditions on being pivotal (i.e., she assumes her vote will determine the collective outcome). We show that, when the number of agents is large, increasing the threshold for implementing the risky action leads to increased experimentation. However, information regarding the overall desirability of the risky action is effectively aggregated only if the threshold is sufficiently low. 

4.  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.