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

这个教师早期职业发展（CAREER）计划奖的研究目标是研究在动态和不确定的设置双边市场的设计。由于随机到达市场的两侧，系统中的代理人口随着时间的推移而演变。代理商在进入系统后报告他们的偏好，并根据偏好的兼容性以及未来选项的价值接受或拒绝匹配报价。该奖项的主要目标是通过考虑代理的动态交互，不确定性和前瞻性行为，以有效的方式设计此类市场。研究方法是计算每个代理的预期收益，从剩余的不匹配的效用承诺，从市场设计师在给定的政策，这将使算法（动态）个别合理的匹配提供。该算法将被推广到代理可能不知道他们的真实偏好的匹配之前，有多个匹配双方产生的环境。进一步的分析将赋予代理人诚实行为的直接激励，并考虑市场设计者通过观察他们对匹配报价的反应来了解代理人偏好的可能性，而代理人通过观察他们得到的报价来了解可用合作伙伴的分布。这项研究的结果将导致市场布局的改进，在这些市场中，需要匹配诸如劳动力市场，器官移植，儿童收养和寄养该奖项可以提供建模和分析工具，以最大限度地提高匹配系统的效率和参与者的福利。通过结合经济学中的匹配/市场设计和业务中常用的工具，使研究成为可能。开发的数学还将有助于培养研究生和本科生，并有助于卡内基梅隆大学和宾夕法尼亚州收养交易所（PAE）之间正在进行的公私伙伴关系。

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).