Optimal Allocation Without Money: An Engineering Approach

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.