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 [1].
Best of Both Worlds: Agents with Entitlements
Authors: Martin Hoefer, Marco Schmalhofer, Giovanna Varricchio
JAIR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper focuses on theoretical contributions in fair division, presenting algorithms, proofs, lemmas, and theorems. For instance, it states: "Our main result is a lottery for additive valuations and different entitlements that is ex-ante weighted envy-free (WEF), as well as ex-post weighted proportional up to one good (WPROP1) and weighted transfer envy-free up to one good (WEF(1, 1)). We show that this result is tight ex-ante WEF is incompatible with any stronger ex-post WEF relaxation." There are no sections describing experimental setups, datasets, performance metrics, or empirical validation. |
| Researcher Affiliation | Academia | All authors are affiliated with universities: "RWTH Aachen University, Germany", "Goethe University Frankfurt, Germany", and "University of Calabria, Italy". |
| Pseudocode | Yes | The paper includes a clearly labeled algorithm block: "Algorithm 1: Different Speeds Eating" on page 10. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide links to code repositories or supplementary materials for code. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any publicly available datasets. It uses a conceptual 'fair division instance I' for examples (e.g., Example 1 on page 5, Table 1 on page 6) with arbitrarily defined agents and goods, not real-world or simulated data for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments using datasets, therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithms, proofs, and properties of fairness criteria. It does not describe any experimental setup that would require specific hardware, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software or programming libraries with version numbers used for implementation or experiments. |
| Experiment Setup | No | The paper is theoretical and does not include any experimental results or setups, thus there are no details about hyperparameters, training configurations, or system-level settings. |