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].
Strategyproof Mechanisms for Additively Separable and Fractional Hedonic Games
Authors: Michele Flammini, Bojana Kodric, Gianpiero Monaco, Qiang Zhang
JAIR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We focus on social welfare maximization and provide several lower and upper bounds on the performance achievable by strategyproof mechanisms for general and specific additive functions. In most of the cases we provide tight or asymptotically tight results. All our mechanisms are simple and can be run in polynomial time. Moreover, all the lower bounds are unconditional, that is, they do not rely on any computational complexity assumptions. |
| Researcher Affiliation | Collaboration | Michele Flammini EMAIL Gran Sasso Science Institute L Aquila, Italy Bojana Kodric EMAIL Gran Sasso Science Institute L Aquila, Italy Gianpiero Monaco EMAIL University of L Aquila L Aquila, Italy Qiang Zhang EMAIL Borealis AI Toronto, Canada. |
| Pseudocode | Yes | Mechanism M1. Given an undirected weighted graph G = (V, E, w) where w : E R, the mechanism performs as follows: 1 Create a complete graph G = (V, E , w ) by adding edges of weight 0 to G. 2 Consider any fixed numbering of the nodes in V , where |V | = n, and represent each matching as a binary vector (x12, x13, . . . , x23, x24, . . . , xn 1n) in {0, 1}(n 2), and let be the lexicographic order on these vectors. 3 Return the -minimal matching from the set argmax M M P {i,j} M w (i, j). |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code, nor does it include a link to a code repository. The text only mentions open problems and future work, without any commitment to code availability. |
| Open Datasets | No | The paper is theoretical and focuses on mechanisms for hedonic games and valuation functions. It does not use any specific real-world or benchmark datasets for empirical evaluation, nor does it provide access to any data. |
| Dataset Splits | No | The paper does not involve empirical experiments using datasets, therefore, there is no mention of dataset splits such as training, validation, or test sets. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup that would require specific hardware. It mentions that mechanisms can be run in 'polynomial time' but does not specify any hardware used for this. |
| Software Dependencies | No | The paper does not mention any specific software, libraries, or programming languages with version numbers that would be required to reproduce the work. The mechanisms are described conceptually without implementation details. |
| Experiment Setup | No | The paper focuses on theoretical analysis and algorithm design without presenting empirical experiments. Therefore, there are no details regarding experimental setup, hyperparameters, or training configurations. |