Affiliation:
1. UNSW Sydney and Data61 CSIRO, Australia
2. University of Patras, Greece
3. University of Tokyo, Japan
4. TU Berlin, Germany
Abstract
We consider the problem of fairly dividing a set of items. Much of the fair division literature assumes that the items are ``goods'' i.e., they yield positive utility for the agents. There is also some work where the items are ``chores'' that yield negative utility for the agents. In this paper, we consider a more general scenario where an agent may have negative or positive utility for each item. This framework captures, e.g., fair task assignment, where agents can have both positive and negative utilities for each task. We show that whereas some of the positive axiomatic and computational results extend to this more general setting, others do not. We present several new and efficient algorithms for finding fair allocations in this general setting. We also point out several gaps in the literature regarding the existence of allocations satisfying certain fairness and efficiency properties and further study the complexity of computing such allocations.
Publisher
International Joint Conferences on Artificial Intelligence Organization
Cited by
19 articles.
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