🤖 AI Summary
This work proposes a novel paradigm for dynamic market design that overcomes the limitations of traditional static models, which struggle to accommodate the asynchronous and stochastic arrival of supply and demand in digital platforms. By mapping dynamic market problems onto a static planning framework under steady-state conditions, the approach integrates steady-state analysis of Markov processes, mechanism design theory, and queueing theory. It jointly optimizes priority rules, information policies, and queueing mechanisms—with and without monetary transfers—to achieve intertemporal matching and market clearing. The framework effectively screens participants and balances intertemporal supply and demand without relying on price mechanisms, thereby significantly enhancing the efficiency and stability of dynamic markets in settings characterized by information constraints and asynchronous arrivals.
📝 Abstract
Classic market design theory is rooted in static models where all participants trade simultaneously. In contrast, modern platform-mediated digital markets are fundamentally dynamic, defined by the asynchronous and stochastic arrival of supply and demand. This chapter surveys recent work that brings market design to this dynamic setting. We focus on a methodological framework that transforms complex dynamic problems into tractable static programs by analyzing the long-run stationary distribution of the system. The survey explores how priority rules and information policy can be designed to clear markets and screen agents when monetary transfers are unavailable, and, when they are available, how queues of participants and goods can be managed to balance intertemporal mismatches of demand and supply and to spread competitive pressures across time.