Email: sato.hiroto.s9(at)f.mail.nagoya-u.ac.jp
I am a postdoctoral researcher at Nagoya University. I am a microeconomic theorist with a focus on information and uncertainty.
I received my PhD in Economics from the University of Tokyo in 2024.
We study a simple problem of allocating common-value goods. The designer seeks to allocate the goods to as many unit-demand agents as possible without monetary transfers, while agents, who possess partial private information about the goods, are willing to receive them only when the goods are of high value. Mechanisms screen each agent's private information using the information of other agents, and in doing so shape what agents learn from other agents about the value of the goods. The optimal mechanism can be summarized by two parameters: one adjusts the allocation probability, while the other governs the amount of learning induced by allocation. Although the designer prefers to allocate the goods, the optimal mechanism excludes some agents and, as a result, may withhold allocation even when all agents would be willing to receive them. The optimal mechanism has the same structure even when payments are available, but it may not exclude any agent and may involve strictly positive payments that are decreasing in allocation.
This study extends Blackwell's (1953) comparison of information to a sequential social learning model, where agents make decisions sequentially based on both private signals and the observed actions of others. In this context, we introduce a new binary relation over information structures: An information structure is more socially valuable than another if it yields higher expected payoffs for all agents, regardless of their preferences. First, we establish that this binary relation is strictly stronger than the Blackwell order. Then, we provide a necessary and sufficient condition for our binary relation and propose a simpler sufficient condition that is easier to verify.
Consider a situation wherein a decision maker sequentially searches for the best alternative among heterogeneous options with an arbitrary search order. The agent partially learns the value of an option when inspecting it. We characterize the set of all search behaviors compatible with some information structure, which forms a polytope. A single information structure rationalizes all these search behaviors, which minimizes the agent's welfare among all information structures. Under certain symmetry assumption over primitives, we also examine the set of all feasible pairs of search and choice behaviors, with and without option sellers' price competition, and prove the same results. These findings provide sharp insights not only for information design but also for the identification problem.
( This paper subsumes Information Design in Pandora's Problem )
In social learning environments, agents acquire information from both private signals and the observed actions of predecessors, referred to as history. We define the value of history as the gain in expected payoff from accessing both the private signal and history, compared to relying on the signal alone. We first characterize the information structures that maximize this value, showing that it is highest under a mixture of full information and no information. We then apply these insights to a model of markets for history, where a monopolistic data seller collects and sells access to history. In equilibrium, the seller's dynamic pricing becomes the value of history for each agent. This gives the seller incentives to increase the value of history by designing the information structure. The seller optimal information discloses less information than the socially optimal level.
In many economic situations, such as job search and online shopping, agents are sequentially searching for information to choose one of a few options. Information revealed through their search process affects the eventual choice outcomes of such economies. This study explres a Bayesian persuasion problem in Weitzman's (1979) ordered search models. We show that an optimal signal structure consists of three signals for any risk-neutral planner. Neither providing no information nor full information is optimal except for trivial cases. We further derive comparative statics results for the tight bounds of each option's chosen probability and find that Bayesian persuasion minimizes agents' welfare in many cases.
Priority uncertainty is prevalent in practical matching markets. This study investigates the role of priority information structures in a simple decentralized college admissions model. The first main theorem characterizes equilibrium distributions of students across schools, which are implementable with a class of simple disclosure rules, cutoff signals. The cutoff signal induces an ex-ante fair allocation that is also the closest to being ex-post fair among the allocations achieving the same distribution. As an application, we consider an information design problem. The second main theorem shows that each equilibrium distribution is implementable as a unique equilibrium.
This paper studies sequential information design (Doval and Ely 2020) in which a designer can construct the extensive form along with the information structure. In this framework, I investigate robust implementations against adversarial equilibrium selection, when players and the designer have a supermodular payoff function with dominant states and an outside option. The main results show that the optimal partially implementable outcome is fully implementable in sequential information design, which essentially coincides with the optimal partially implementable outcome in static information design. For economic applications such as global game of regime change, this paper proposes a way to robustly achieve the desired outcome in static information design by providing the extensive form and the information structure.
Download: Curriculum Vitae (PDF)