Strategyproof and Approximately Maxmin Fair Share Allocation of Chores

Strategyproof and Approximately Maxmin Fair Share Allocation of Chores

Haris Aziz, Bo Li, Xiaowei Wu

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

We initiate the work on fair and strategyproof allocation of indivisible chores. The fairness concept we consider in this paper is maxmin share (MMS) fairness. We consider three previously studied models of information elicited from the agents: the ordinal model, the cardinal model, and the public ranking model in which the ordinal preferences are publicly known. We present both positive and negative results on the level of MMS approximation that can be guaranteed if we require the algorithm to be strategyproof. Our results uncover some interesting contrasts between the approximation ratios achieved for chores versus goods.
Keywords:
Agent-based and Multi-agent Systems: Computational Social Choice
Agent-based and Multi-agent Systems: Resource Allocation