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..
Fair Division of Mixed Divisible and Indivisible Goods
Authors: Xiaohui Bei, Zihao Li, Jinyan Liu, Shengxin Liu, Xinhang Lu1814-1821
AAAI 2020 | Venue PDF | 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. |