Strategyproof Allocation Mechanisms with Endowments and M-convex Distributional Constraints

Takamasa Suzuki, Akihisa Tamura, Kentaro Yahiro, Makoto Yokoo, Yuzhe Zhang

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


We consider an allocation problem of multiple types of objects to agents, where each type of object has multiple copies (e.g., multiple seats in a school), each agent is endowed with an object, and some distributional constraints are imposed on the allocation (e.g., minimum/maximum quotas). We develop two mechanisms that are strategyproof, feasible (they always satisfy distributional constraints), and individually rational, assuming the distributional constraints are represented by an M-convex set. One mechanism, based on Top Trading Cycles, is Pareto efficient; the other, which belongs to the mechanism class specified by Kojima et al. [1], satisfies a relaxed fairness requirement. The class of distributional constraints we consider contains many situations raised from realistic matching problems, including individual minimum/maximum quotas, regional maximum quotas, type-specific quotas, and distance constraints. Finally, we experimentally evaluate the performance of these mechanisms by a computer simulation.

Original languageEnglish
Article number103825
JournalArtificial Intelligence
Publication statusPublished - Feb 2023

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence


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