論文

This study explores the dual role of electric vehicles (EVs) in mobility service and electricity markets through vehicle-to-grid (V2G) technology, enabling EVs to stabilize energy systems by trading electricity with the grid. Building upon Kelso and Crawford's [4] algorithm, we develop an EV-proposing price-adjustment process to determine matchings between EVs and either riders or charging stations, along with their corresponding transaction prices. We demonstrate that the proposed algorithm produces stable outcomes in both markets. Furthermore, to assess the impact of V2G-enabled EVs, simulations were conducted within a virtual transportation network. The results show that electricity-coordinating EVs play a more significant role in stabilizing the electricity market and, when the station battery levels are low, generate a higher total surplus compared to scenarios without such coordination. In other situations, managing the roles of EVs is essential to maximize welfare.

We report an experiment on the relationship between sleep and cooperative behavior by using Electroencephalogram (EEG) measuring devices and Psychomotor Vigilance Test (PVT) in measuring subjects' alertness. In this experiment, two types of games - prisoner's dilemmas and coordination games - were conducted by controlling subjects' sleep. We found that lower alertness measured by PVT entails more cooperation in both games. On the other hand, we did not observe a significant relationship between sleeping time and cooperation.

© 2018 Elsevier Inc. We study a college admissions problem in which colleges accept students by ranking students’ efforts in entrance exams. Students’ ability levels affect the cost of their efforts. We solve and compare equilibria of “centralized college admissions” (CCA) where students apply to all colleges and “decentralized college admissions” (DCA) where students only apply to one college. We show that lower ability students prefer DCA whereas higher ability students prefer CCA. Many predictions of the theory are supported by a lab experiment designed to test the theory, yet we find a number of differences that render DCA less attractive than CCA compared to the equilibrium benchmark.

© 2019 Elsevier Inc. In one-sided matching/assignment problems, an important debate centers around whether it is possible to improve upon the Gale-Shapley student-proposing deferred acceptance mechanism (DA) via alternative strategy-proof mechanisms. In unrestricted preference domains, no strategy-proof mechanism Pareto improves upon DA whether students have outside options or not. When standard exogenous outside options, e.g., private school, do not necessarily exist, we show that endogenous outside options, i.e., problem-specific schools that could always accept a deviating student, arise and the impossibility of obtaining a strategy-proof improvements over DA prevails. It is, however, possible to construct natural subdomains allowing for positive results, where some students' preferences are in part induced by an exogenous hierarchy of quality tiers. We then identify maximal domains on which it is possible to improve upon DA without sacrificing strategy-proofness. This result may help better assess the underpinnings of the three-way tension among efficiency, individual rationality/stability, and strategy-proofness in matching problems.

© 2019, Japanese Economic Association. We introduce a dynamic framework to analyze two-sided matching interactions that occur repeatedly over time, such as teacher–student matching or hospital–intern markets in Britain. We propose a dynamic concept of credible group stability and show that implementing a men-optimal stable matching in each period is credibly group-stable. The result holds for a women-optimal stable matching. A credibly group-stable dynamic matching is individually rational and immune to any defensible group deviations with an appropriate definition of defensibility. We obtain several policy implications for market design. Moreover, a sufficient condition for Pareto efficiency is given for finitely repeated markets.

Allocating appointment slots is presented as a new application for market design. Online booking systems are commonly used by public authorities to allocate appointments for visa interviews, driver's licenses, passport renewals, etc. We document that black markets for appointments have developed in many parts of the world. Scalpers book the appointments that are offered for free and sell the slots to appointment seekers. We model the existing first-come-first-served booking system and propose an alternative batch system. The batch system collects applications for slots over a certain time period and then randomly allocates slots to applicants. The theory predicts and lab experiments confirm that scalpers profitably book and sell slots under the current system with sufficiently high demand, but that they are not active in the proposed batch system. We discuss practical issues for the implementation of the batch system and its applicability to other markets with scalping.