Maximin-Aware Allocations of Indivisible Chores with Symmetric and Asymmetric Agents
Authors: Tianze Wei, Bo Li, Minming Li
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | 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. |