Approximate Envy-Freeness in Graphical Cake Cutting

Authors: Sheung Man Yuen, Warut Suksompong

IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical 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 1/2additive-envy-free allocation and, if the agents valuations are identical, a (2 + ϵ)-multiplicativeenvy-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.
Researcher Affiliation Academia Sheung Man Yuen and Warut Suksompong National University of Singapore {yuens,warut}@comp.nus.edu.sg
Pseudocode Yes Algorithm 1 ITERATIVEDIVIDE(G, N)., Algorithm 2 RECURSIVEBALANCE(A, ϵ)., Algorithm 3 BALANCE(A, ϵ)., Algorithm 4 BALANCEPATH(P, ϵ).
Open Source Code No The paper does not provide an explicit statement or link to open-source code for the described methodology.
Open Datasets No The paper is theoretical and does not involve the use of datasets for training.
Dataset Splits No The paper is theoretical and does not discuss training, test, or validation dataset splits.
Hardware Specification No The paper is theoretical and does not describe the hardware used for experiments.
Software Dependencies No The paper does not mention specific software dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not include details about an experimental setup.