An Improved Quasi-Polynomial Algorithm for Approximate Well-Supported Nash Equilibria
Authors: Michail Fasoulakis, Evangelos Markakis1926-1932
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 mfasoul@ics.forth.gr Evangelos Markakis Department of Informatics, Athens University of Economics and Business, Greece markakis@aueb.gr |
| 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. |