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..
Leadership in Congestion Games: Multiple User Classes and Non-Singleton Actions
Authors: Alberto Marchesi, Matteo Castiglioni, Nicola Gatti
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, for both settings, we provide mixed-integer linear programming formulations, and we experimentally evaluate their scalability on both random game instances and worst-case instances based on our hardness reductions. [...] 7 Experimental Results [...] We test the MILP formulations proposed in Section 6 on randomly generated games, which represent instances of average-case complexity, and games based on the reductions provided in the proofs of Theorems 1 and 6, which, instead, represent worst-case complexity instances. All the experiments are run on a UNIX machine with a total of 32 cores working at 2.3 GHz, equipped with 128 GB of RAM. Each instance is solved on a single core within a time limit of 3600 seconds. We use GUROBI 7.0 (with Python interface) as MILP solver. |
| Researcher Affiliation | Academia | Alberto Marchesi , Matteo Castiglioni and Nicola Gatti Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, Italy EMAIL |
| Pseudocode | No | The paper describes a dynamic programming algorithm via a recursive equation (Lemma 4) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using 'GUROBI 7.0 (with Python interface) as MILP solver' but does not provide any specific access to source code for the methodology described in the paper. |
| Open Datasets | No | The paper describes generating 'random game instances' and 'worst-case instances' but does not provide concrete access information (link, DOI, repository, formal citation) for publicly available or open datasets. |
| Dataset Splits | No | The paper generates random game instances and worst-case instances but does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | Yes | All the experiments are run on a UNIX machine with a total of 32 cores working at 2.3 GHz, equipped with 128 GB of RAM. |
| Software Dependencies | Yes | We use GUROBI 7.0 (with Python interface) as MILP solver. |
| Experiment Setup | Yes | For T -class SSCGs, we generate random game instances with r {10, 20, 30, 40, 50, 60, 70, 80, 90, 100} resources and T {1, 2, 3, 4} classes, with nt {0.2 r, 0.5 r, r} followers per class t T and |At| = 0.5 r actions per class t T . Cost functions are randomly generated by sampling uniformly from {1, . . . , nr T}. For general SCGs, we generate instances with r {5, 10, 15, 20, 25} resources and n {r, 2 r, 3 r} followers, with |ap| {1, 2, 3, 4, 5} resources per action ap Ap and |Ap| = 0.5 r actions per player p N. Cost functions are randomly generated by sampling uniformly from {1, . . . , nr}. We build a testbed with 20 game instances per combination of the parameters. |