arXiv subjects
Publications and source records attributed to Kailin Chen.
This note presents two results. First, it shows that under mild conditions, a decision problem is quasi-concave if the set of optimal actions is convex under every belief. Second, it shows that if a decision problem is quasi-concave, then it satisfies the local single crossing property after relabeling the states.
This paper studies an exponential bandit model in which a group of agents collectively decide whether to undertake a risky action $R$. This action is implemented if the fraction of agents voting for it exceeds a predetermined threshold $k$. Building on Strulovici (2008), 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 $R$; 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 $k$ for implementing $R$ leads to increased experimentation. However, information regarding the overall desirability of $R$ is effectively aggregated only if $k$ is sufficiently low.
This paper analyzes a cheap-talk model with multiple senders and one receiver. Each sender observes a noisy signal about an unknown state and sends a message; the receiver observes the message tally and chooses a policy. This setting shares certain features with voting models (e.g., Feddersen and Pesendorfer, 1997, 1998). The existing literature (e.g., Levit and Malenko, 2011; Battaglini, 2017) focuses on scenarios in which the receiver and the senders agree on the preferred policy in each state. In contrast, we explore environments in which the receiver and the senders disagree over the preferred policy in some states. We establish an equilibrium no-conflict result: in any non-babbling equilibrium, the senders and the receiver agree on the preferred policy at every realized message tally. We show that information aggregation fails, and the receiver cannot fully learn the state even as the number of senders grows large. We also identify a discontinuity in information transmission relative to the implications of the existing literature. Finally, introducing a mediator can improve information transmission and restore efficiency.
This paper introduces a novel ranking of statistical experiments, the linear-Blackwell (LB) order, which can equivalently be characterized by (i) the dispersion of the induced posterior and likelihood ratios in the sense of the linear convex order, (ii) the size of the Lorenz zonoid (the set of statewise expectation profiles), or (iii) the variability of the posterior mean. We apply the LB order to compare experiments in binary-action decision problems and in decision problems with quasi-concave payoffs, as analyzed by Kolotilin, Corrao, and Wolitzky (2025). We also use it to compare experiments in moral hazard problems, building on Holmstr\"om (1979) and Kim (1995), and in screening problems with ex post signals.