Fair Division Through Information Withholding


  • Hadi Hosseini Rochester Institute of Technology
  • Sujoy Sikdar Washington University St. Louis
  • Rohit Vaish Rensselaer Polytechnic Institute
  • Hejun Wang Rensselaer Polytechnic Institute
  • Lirong Xia Rensselaer Polytechnic Institute




Envy-freeness up to one good (EF1) is a well-studied fairness notion for indivisible goods that addresses pairwise envy by the removal of at most one good. In the worst case, each pair of agents might require the (hypothetical) removal of a different good, resulting in a weak aggregate guarantee. We study allocations that are nearly envy-free in aggregate, and define a novel fairness notion based on information withholding. Under this notion, an agent can withhold (or hide) some of the goods in its bundle and reveal the remaining goods to the other agents. We observe that in practice, envy-freeness can be achieved by withholding only a small number of goods overall. We show that finding allocations that withhold an optimal number of goods is computationally hard even for highly restricted classes of valuations. In contrast to the worst-case results, our experiments on synthetic and real-world preference data show that existing algorithms for finding EF1 allocations withhold a close-to-optimal amount of information.




How to Cite

Hosseini, H., Sikdar, S., Vaish, R., Wang, H., & Xia, L. (2020). Fair Division Through Information Withholding. Proceedings of the AAAI Conference on Artificial Intelligence, 34(02), 2014-2021. https://doi.org/10.1609/aaai.v34i02.5573



AAAI Technical Track: Game Theory and Economic Paradigms