Assignment Mechanisms Under Distributional Constraints

Itai Ashlagi (), Amin Saberi () and Ali Shameli ()
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Itai Ashlagi: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Amin Saberi: Department of Management Science and Engineering, Stanford University, Stanford, California 94305
Ali Shameli: Department of Management Science and Engineering, Stanford University, Stanford, California 94305

Operations Research, 2020, vol. 68, issue 2, 467-479

Abstract: We generalize the serial dictatorship (SD) and probabilistic serial (PS) mechanism for assigning indivisible objects (seats in a school) to agents (students) to accommodate distributional constraints. Such constraints are motivated by equity considerations. Our generalization of SD maintains several of its desirable properties, including strategyproofness, Pareto optimality, and computational tractability, while satisfying the distributional constraints with a small error. Our generalization of the PS mechanism finds an ordinally efficient and envy-free assignment while satisfying the distributional constraint with a small error. We show, however, that no ordinally efficient and envy-free mechanism is also weakly strategyproof. Both of our algorithms assign at least the same number of students as the optimum fractional assignment.

Keywords: market design; assignment; matching; refugee; strategyproof (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (10)

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