Optimal Allocations under Strongly Pigou-Dalton Criteria: Hidden Layer Structure & Efficient Combinatorial Approach

TL;DR

This paper studies allocating $m$ items to $n$ agents with binary valuations to maximize social welfare while controlling inequality under SPD criteria. It proves that SPD-optimal allocations are exactly the stable allocations and introduces a hidden-layer partition that enables reducing the divisible-items case to the indivisible core, yielding a combinatorial algorithm with time $O(m^2n^5)$ for DIV and a faster $O(m^2n)$-time algorithm for IND. A key consequence is that SPD-optimal profiles are tightly coupled across allocations, with Chebyshev distance at most 1 (and 0 in the DIV setting), and the layer structure clarifies which items and agents are bound to particular layers. Together, the results unify a broad class of SPD criteria (e.g., LexiMin, Nash welfare) and provide efficient, structure-aware methods for fair allocation under both divisible and indivisible items, advancing the practical rollout of fair division in multi-agent settings.

Abstract

We investigate optimal social welfare allocations of $m$ items to $n$ agents with binary additive or submodular valuations. For binary additive valuations, we prove that the set of optimal allocations coincides with the set of so-called \emph{stable allocations}, as long as the employed criterion for evaluating social welfare is strongly Pigou-Dalton (SPD) and symmetric. Many common criteria are SPD and symmetric, such as Nash social welfare, leximax, leximin, Gini index, entropy, and envy sum. We also design efficient algorithms for finding a stable allocation, including an $O(m^2n)$ time algorithm for the case of indivisible items, and an $O(m^2n^5)$ time one for the case of divisible items. The first is faster than the existing algorithms or has a simpler analysis. The latter is the first combinatorial algorithm for that problem. It utilizes a hidden layer partition of items and agents admitted by all stable allocations, and cleverly reduces the case of divisible items to the case of indivisible items. In addition, we show that the profiles of different optimal allocations have a small Chebyshev distance, which is 0 for the case of divisible items under binary additive valuations, and is at most 1 for the case of indivisible items under binary submodular valuations.

Theorems & Definitions (1)

• definition thmcounterdefinition: Criterion