Optimal Allocation Without Money: An Engineering Approach
研究了无货币支付下异质性服务的分配问题，通过大市场近似求解并转化为可行机制，应用于波士顿学生择校，显著提升了效率、公平性和可预测性。
We study the allocation of heterogeneous services to agents with incomplete information and without monetary transfers. Agents have private, multidimensional utilities over services, drawn from commonly known priors. The social planner’s goal is to maximize a potentially complex public objective. For tractability, we take an “engineering” approach, in which we solve a large-market approximation, and convert the solution into a feasible finite-market mechanism that still yields good results. We apply this framework to real data from Boston to design a mechanism that assigns students to public schools, in order to maximize a linear combination of utilitarian and max-min welfare, subject to capacity and transportation constraints. We show how to optimally solve a large-market formulation with more than 868 types of students and 77 schools, and we translate the solution into a finite-market mechanism that significantly outperforms the baseline plan chosen by the city in terms of efficiency, equity, and predictability. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2162 . This paper was accepted by Martin Lariviere, operations management.