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..
An Improved Quasi-Polynomial Algorithm for Approximate Well-Supported Nash Equilibria
Authors: Michail Fasoulakis, Evangelos Markakis1926-1932
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our algorithm is based on appropriately combining sampling arguments, support enumeration, and solutions to systems of linear inequalities. ... The complexity of the algorithm is n O(log log n 1 ε /ε2), where n is the number of available pure strategies to the players. |
| Researcher Affiliation | Academia | Michail Fasoulakis Institute of Computer Science, Foundation for Research and Technology-Hellas (ICS-FORTH), Greece EMAIL Evangelos Markakis Department of Informatics, Athens University of Economics and Business, Greece EMAIL |
| Pseudocode | Yes | Algorithm 1 Input: A bimatrix game (R, C) [0, 1]n n and a parameter ε (0, 1]. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | This paper is theoretical, presenting an algorithm and its complexity analysis. It does not use datasets for training, validation, or testing, nor does it refer to any publicly available datasets. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments with datasets. Therefore, no dataset splits for validation are mentioned. |
| Hardware Specification | No | This is a theoretical paper focusing on algorithm design and complexity analysis. It does not mention any hardware used for experiments. |
| Software Dependencies | No | The paper describes an algorithm that involves solving systems of linear inequalities, but it does not specify any particular software, library, or solver with version numbers. |
| Experiment Setup | No | This is a theoretical paper describing an algorithm and its properties. It does not detail any experimental setup, hyperparameters, or training configurations, as no empirical experiments are performed. |