Fair Allocation with Special Externalities

TL;DR

This paper explores fair division when agents are affected by externalities, proposing adaptations of existing algorithms for certain fairness notions, but highlighting challenges with others like MMS in this context.

Contribution

It introduces a model for externalities where agents value unassigned items independently, and adapts algorithms to achieve fairness and efficiency under this model.

Findings

Adapted algorithms ensure certain fairness notions with externalities.

Proportionality fairness needs redefinition in this setting.

MMS may lack any multiplicative approximation with externalities.

Abstract

Most of the existing algorithms for fair division do not consider externalities. Under externalities, the utility an agent obtains depends not only on its allocation but also on the allocation of other agents. An agent has a positive (negative) value for the assigned goods (chores). This work focuses on a special case of externality, i.e., an agent receives positive or negative value for unassigned items independent of which other agent gets it. We show that it is possible to adapt existing algorithms using a transformation to ensure certain fairness and efficiency notions in this setting. Despite the positive results, fairness notions like proportionality need to be re-defined. Further, we prove that maximin share (MMS) may not have any multiplicative approximation in this setting. Studying this domain is a stepping stone towards full externalities where ensuring fairness is much more challenging.