CAREER: Design of Two-sided Markets In Dynamic and Uncertain Environments: Theoretical and Operational Issues

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

The research objective of this Faculty Early Career Development (CAREER) Program award is to study the design of two-sided markets in dynamic and uncertain settings. The population of agents in the system evolves over time due to stochastic arrivals to both sides of the market. Agents report their preferences upon entering the system, and either accept or reject match offers based on the compatibility of preferences as well as value of future options. The primary goal of this award is to design such marketplaces in an efficient way by taking into account dynamic interaction, uncertainty and forward looking behavior of agents. The research approach is to compute each agent's expected benefit from remaining unmatched as a utility promise from the market designer under a given policy, which will enable an algorithm for (dynamically) individually-rational match offers. The algorithm will be generalized to the environment where agents may not know their true preferences over matches before the match and there are multiple matches generated on both sides. Further analysis will endow agents with straightforward incentives for truthful behavior and consider the possibility that a market designer learns about agent preferences by observing their responses to match offers while the agents learn about the distribution of available partners by observing the offers they get. If successful, the results of this research will lead to improvements in the layout of markets where there is a need to match two-sided populations of agents such as labor markets, organ transplant, child adoption, and foster care. The award can provide modeling and analysis tools to maximize the efficiency of the matching system and the welfare of its participants. The research is made possible by combining tools developed both in matching/market design in economics and those commonly used in operations. The mathematics developed will also help train graduate and undergraduate students as well as contribute to an ongoing private public partnership between Carnegie Mellon and Pennsylvania Adoption Exchange (PAE).