Discrepancy Beyond Additive Functions with Applications to Fair Division
Abstract
We consider a setting where we have a ground set together with real-valued set functions , and the goal is to partition into two sets such that is small for every . Many results in discrepancy theory can be stated in this form with the functions being additive. In this work, we initiate the study of the unstructured case where is not assumed to be additive. We show that even without the additivity assumption, the upper bound remains at most .
Our result has implications on the fair allocation of indivisible goods. In particular, we show that a consensus halving up to goods always exists for agents with monotone utilities. Previously, only an bound was known for this setting.
Keywords and phrases:
Discrepancy Theory, Fair DivisionCategory:
Extended AbstractFunding:
Raghu Meka: Supported by the NSF Award CCF-2217033 (EnCORE: Institute for Emerging CORE Methods in Data Science).
Warut Suksompong: Supported by the Singapore Ministry of Education under grant number MOE-T2EP20221-0001 and by an NUS Start-up Grant.

Leibniz International Proceedings in Informatics