Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Almost Group Envy-free Allocation of Indivisible Goods and Chores
Authors: Haris Aziz, Simon Rey
IJCAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,404 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,220 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We then present a clear taxonomy of the fairness concepts. We study which fairness concepts guarantee the existence of a fair allocation under which preference domain. For two natural classes of additive utilities, we design polynomial-time algorithms to compute a GEF1 allocation. We also prove that checking whether a given allocation satisfies GEF1 is co NP-complete when there are either only goods, only chores or both. We prove that checking whether a given allocation satisfies GEF1 is co NP-complete for the cases of only goods, only chores and both (Section 6). |
| Researcher Affiliation | Collaboration | Haris Aziz1,2 and Simon Rey3 1UNSW Sydney 2Data61 CSIRO 3Institute for Logic, Language and Computation (ILLC), University of Amsterdam |
| Pseudocode | Yes | Algorithm 1 The Egal-Sequential Algorithm and Algorithm 2 The Ternary Flow Algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | This paper is theoretical and does not conduct empirical studies involving datasets, training, or evaluation. |
| Dataset Splits | No | This paper is theoretical and does not describe empirical experiments that would involve dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or mention specific hardware used. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers required for replication. |
| Experiment Setup | No | The paper is theoretical and does not provide details about experimental setup, hyperparameters, or system-level training settings. |