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].
Non-Obvious Manipulability in Additively Separable and Fractional Hedonic Games
Authors: Diodato Ferraioli, Giovanna Varricchio
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We first prove that, when scores can be arbitrary, every optimal mechanism is NOM; moreover, when scores are limited in a continuous interval, an optimal mechanism that is NOM exists. Given the hardness of computing optimal outcomes in these settings, we turn our attention to efficient and NOM mechanisms. To this aim, we first prove a characterization of NOM mechanisms that simplifies the class of mechanisms of interest. Then, we design a NOM mechanism returning approximations that asymptotically match the bestknown approximation achievable in polynomial time. Finally, we focus on discrete scores, where the compatibility of NOM with optimality depends on the specific values. Therefore, we initiate a systematic analysis to identify which discrete values support this compatibility and which do not. |
| Researcher Affiliation | Academia | 1 University of Salerno, Italy 2 University of Calabria, Italy EMAIL, EMAIL |
| Pseudocode | No | The paper describes a mechanism (Mechanism M1) in paragraph text in Section 4.3, but does not provide it in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the methodology described, nor does it provide links to any code repositories. |
| Open Datasets | No | The paper is theoretical and focuses on game theory mechanisms. It does not use or refer to any specific empirical datasets with access information. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on datasets, therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup requiring specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementations or dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup. Therefore, no specific experimental setup details such as hyperparameter values or training configurations are provided. |