Approximate Envy-Freeness in Graphical Cake Cutting
Sheung Man Yuen, Warut Suksompong
TL;DR
The paper tackles graphical cake cutting where the resource is a connected graph and allocations must be connected, showing that exact envy-freeness may fail in this setting. It establishes constructive, polynomial-time algorithms that achieve strong approximate fairness: a $\tfrac{1}{2}$-additive-EF allocation for general graphs, and a $(2+\epsilon)$-EF allocation for identical valuations on general graphs, with even stronger results for star graphs ($3+\epsilon$-EF for arbitrary valuations and $2$-EF for identical valuations). It also develops a layered approach for identical valuations using adaptive thresholds and balance-path techniques to reach $(2+\epsilon)$-EF, and introduces a bag-filling method for stars to obtain exact $2$-EF, all within polynomial time. Beyond single-piece allocations, the work defines the path similarity number to relate connected allocations to interval results, offering a route to multi-piece fair sharing. The study closes with open questions about constant multiplicative envy on general graphs with non-identical valuations and further improvements via path-based reductions.
Abstract
We study the problem of fairly allocating a divisible resource in the form of a graph, also known as graphical cake cutting. Unlike for the canonical interval cake, a connected envy-free allocation is not guaranteed to exist for a graphical cake. We focus on the existence and computation of connected allocations with low envy. For general graphs, we show that there is always a $1/2$-additive-envy-free allocation and, if the agents' valuations are identical, a $(2+ε)$-multiplicative-envy-free allocation for any $ε> 0$. In the case of star graphs, we obtain a multiplicative factor of $3+ε$ for arbitrary valuations and $2$ for identical valuations. We also derive guarantees when each agent can receive more than one connected piece. All of our results come with efficient algorithms for computing the respective allocations.
