Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Maximin-Aware Allocations of Indivisible Chores with Symmetric and Asymmetric Agents
Authors: Tianze Wei, Bo Li, Minming Li
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | A string of results on the existence and computation of MMA related fair allocations, and their connections to existing fairness concepts is given. We study the maximin-aware (MMA) allocation of indivisible chores among n agents whose cost functions are additive. We summarize our problem and the results in the following section. We plot the approximation ratios in Figure 2. As we can see, since 1 n 1 < λ < 2 n 1, the approximation ratio becomes close to 1 when n becomes large. Algorithm 1 computes a (1 + λ)-MMAX allocation, where when n = 2, λ = 2 and when n 3, λ = 3 n+ n2+10n 7 4n 4 ( 1 n 1 < λ < 2 n 1). |
| Researcher Affiliation | Academia | 1Department of Computer Science, City University of Hong Kong 2Department of Computing, The Hong Kong Polytechnic University 3School of Mathematical Sciences, Ocean University of China |
| Pseudocode | Yes | Algorithm 1: Swap algorithm Input: A PROPX allocation X = (X1, . . . , Xn) Output: An approximate MMAX allocation X |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, so it does not provide access information for a public dataset for training. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |