Probabilistic assignment: an extension approach

Abstract

We study the problem of allocating objects using lotteries when agents only submit preferences over objects. A standard approach is to "extend" agents' preferences over objects to preferences over lotteries, using (first-order) stochastic dominance, or the sd-extension. Following (Cho, Games Econ Behav 95:168-177, 2016a), we complement this approach with two alternative extensions, the dl- and ul- extensions, that give rise to lexicographic preferences (dl stands for "downward lexicographic" and ul for "upward lexicographic") and apply all three of them in tandem to probabilistic assignment. Each property of rules now has three versions that vary with the extension chosen. We introduce a family of rules that generalizes the probabilistic serial rule. Then we study their behavior, as well as that of the random priority rule, in terms of efficiency, no-envy, and strategy-proofness.