Efficient Splitting of Measures and Necklaces

TL;DR

This paper develops efficient algorithms for approximate measure and necklace splitting problems, including online solutions, with proven bounds on the number of cuts needed, advancing the understanding of fair division under computational constraints.

Contribution

It introduces new online and offline algorithms for approximate splitting problems, providing bounds on cuts and ensuring fairness, with optimality results in online settings.

Findings

Efficient online algorithms for measure and necklace splitting.

Offline algorithms with logarithmic cut bounds.

Lower bounds showing optimality of online algorithms.

Abstract

We provide approximation algorithms for two problems, known as NECKLACE SPLITTING and $\epsilon$-CONSENSUS SPLITTING. In the problem $\epsilon$-CONSENSUS SPLITTING, there are $n$ non-atomic probability measures on the interval $[0, 1]$ and $k$ agents. The goal is to divide the interval, via at most $n (k-1)$ cuts, into pieces and distribute them to the $k$ agents in an approximately equitable way, so that the discrepancy between the shares of any two agents, according to each measure, is at most $2 \epsilon / k$. It is known that this is possible even for $\epsilon = 0$. NECKLACE SPLITTING is a discrete version of $\epsilon$-CONSENSUS SPLITTING. For $k = 2$ and some absolute positive constant $\epsilon$, both of these problems are PPAD-hard. We consider two types of approximation. The first provides every agent a positive amount of measure of each type under the constraint of making at most $n (k - 1)$ cuts. The second obtains an approximately equitable split with as few cuts as possible. Apart from the offline model, we consider the online model as well, where the interval (or necklace) is presented as a stream, and decisions about cutting and distributing must be made on the spot. For the first type of approximation, we describe an efficient algorithm that gives every agent at least $\frac{1}{nk}$ of each measure and works even online. For the second type of approximation, we provide an efficient online algorithm that makes $\text{poly}(n, k, \epsilon)$ cuts and an offline algorithm making $O(nk \log \frac{k}{\epsilon})$ cuts. We also establish lower bounds for the number of cuts required in the online model for both problems even for $k=2$ agents, showing that the number of cuts in our online algorithm is optimal up to a logarithmic factor.