When agents choose bundles autonomously: guarantees beyond discrepancy

2026-02-11

AI Summary

This work addresses fair allocation of indivisible goods under online settings where agents sequentially select bundles according to a priority order. Existing approaches are constrained by a Θ(√n) lower bound on proportionality (PROP) based on disparity measures. By introducing a constructive partitioning strategy, this paper achieves—for the first time—a universal PROP − O(log n) guarantee in polynomial time, exponentially improving upon the classical barrier. The result is further strengthened for three restricted valuation structures: near-identical ordering, bounded value multiplicities, and limited hypergraph-induced externalities. The approach integrates combinatorial optimization, online algorithms, and fairness theory, modeling inter-agent valuation dependencies via hypergraphs and leveraging priority-based autonomous selection to circumvent global coordination. This framework significantly outperforms traditional bounds while maintaining computational tractability.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Multiagent Systems: Mechanism Design
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We consider the fair division of indivisible items among $n$ agents with additive non-negative normalized valuations, with the goal of obtaining high value guarantees, that is, close to the proportional share for each agent. We prove that partitions where \emph{every} part yields high value for each agent are asymptotically limited by a discrepancy barrier of $\Theta(\sqrt{n})$. Guided by this, our main objective is to overcome this barrier and achieve stronger individual guarantees for each agent in polynomial time. Towards this, we are able to exhibit an exponential improvement over the discrepancy barrier. In particular, we can create partitions on-the-go such that when agents arrive sequentially (representing a previously-agreed priority order) and pick a part autonomously and rationally (i.e., one of highest value), then each is guaranteed a part of value at least $\mathsf{PROP} - \mathcal{O}{(\log n)}$. Moreover, we show even better guarantees for three restricted valuation classes such as those defined by: a common ordering on items, a bound on the multiplicity of values, and a hypergraph with a bound on the \emph{influence} of any agent. Specifically, we study instances where: (1) the agents are ``close''to unanimity in their relative valuation of the items -- a generalization of the ordered additive setting; (2) the valuation functions do not assign the same positive value to more than $t$ items; and (3) the valuation functions respect a hypergraph, a setting introduced by Christodoulou et al. [EC'23], where agents are vertices and items are hyperedges. While the sizes of the hyperedges and neighborhoods can be arbitrary, the influence of any agent $a$, defined as the number of its neighbors who value at least one item positively that $a$ also values positively, is bounded.

Problem

• fair division
• indivisible items
• proportional share
• discrepancy barrier
• autonomous selection

Innovation

• fair division
• indivisible items
• proportional fairness
• sequential allocation
• bounded influence hypergraph