Fair Division of Mixed Divisible and Indivisible Goods
Authors: Xiaohui Bei, Zihao Li, Jinyan Liu, Shengxin Liu, Xinhang Lu1814-1821
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that an EFM allocation always exists for any number of agents. We also propose efficient algorithms to compute an EFM allocation for two agents and for n agents with piecewise linear valuations over the divisible goods. Finally, we relax the envy-free requirement, instead asking for ϵ-envyfreeness for mixed goods (ϵ-EFM), and present an algorithm that finds an ϵ-EFM allocation in time polynomial in the number of agents, the number of indivisible goods, and 1/ϵ. |
| Researcher Affiliation | Academia | 1School of Physical and Mathematical Sciences, Nanyang Technological University 2Institute for Interdisciplinary Information Sciences, Tsinghua University 3Department of Computer Science, The University of Hong Kong |
| Pseudocode | Yes | Algorithm 1 EFM Algorithm, Algorithm 2 EFM Allocation for Two Agents, Algorithm 3 ϵ-EFM Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology, nor does it include links to any code repositories. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and existence proofs for abstract goods ('mixed divisible and indivisible goods', 'cake'), without using any specific real-world or public datasets for empirical evaluation. |
| Dataset Splits | No | The paper does not involve empirical experiments with datasets; therefore, no training, validation, or test dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers relevant to experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs, hence it does not include details on experimental setup or hyperparameters. |