Fair and Efficient Allocations under Lexicographic Preferences


  • Hadi Hosseini Pennsylvania State University
  • Sujoy Sikdar Binghamton University
  • Rohit Vaish Tata Institute of Fundamental Research
  • Lirong Xia Rensselaer Polytechnic Institute




Fair Division, Social Choice / Voting, Mechanism Design


Envy-freeness up to any good (EFX) provides a strong and intuitive guarantee of fairness in the allocation of indivisible goods. But whether such allocations always exist or whether they can be efficiently computed remains an important open question. We study the existence and computation of EFX in conjunction with various other economic properties under lexicographic preferences--a well-studied preference restriction model in artificial intelligence and economics. In sharp contrast to the known results for additive valuations, we not only prove the existence of EFX and Pareto optimal allocations, but in fact provide an algorithmic characterization of these two properties. We also characterize the mechanisms that are, in addition, strategyproof, non-bossy, and neutral. When the efficiency notion is strengthened to rank-maximality, we obtain non-existence and computational hardness results, and show that tractability can be restored when EFX is relaxed to another well-studied fairness notion called maximin share guarantee (MMS).




How to Cite

Hosseini, H., Sikdar, S., Vaish, R., & Xia, L. (2021). Fair and Efficient Allocations under Lexicographic Preferences. Proceedings of the AAAI Conference on Artificial Intelligence, 35(6), 5472-5480. https://doi.org/10.1609/aaai.v35i6.16689



AAAI Technical Track on Game Theory and Economic Paradigms