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..
Tight Inapproximability for Graphical Games
Authors: Argyrios Deligkas, John Fearnley, Alexandros Hollender, Themistoklis Melissourgos
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a complete characterization for the computational complexity of finding approximate equilibria in two-action graphical games. We consider the two most well-studied approximation notions: ε-Nash equilibria (ε-NE) and ε-well-supported Nash equilibria (ε-WSNE), where ε [0, 1]. We prove that computing an ε-NE is PPAD-complete for any constant ε < 1/2, while a very simple algorithm (namely, letting all players mix uniformly between their two actions) yields a 1/2-NE. On the other hand, we show that computing an ε-WSNE is PPAD-complete for any constant ε < 1, while a 1-WSNE is trivial to achieve, because any strategy profile is a 1-WSNE. All of our lower bounds immediately also apply to graphical games with more than two actions per player. |
| Researcher Affiliation | Academia | Royal Holloway, United Kingdom University of Liverpool, United Kingdom EPFL, Switzerland University of Essex, United Kingdom |
| Pseudocode | No | The paper describes algorithms in prose (e.g., 'The algorithm proceeds in two steps. In the first step...'), but does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that open-source code for the described methodology is provided. |
| Open Datasets | No | This paper is theoretical and does not conduct experiments on datasets, thus no information on publicly available datasets or access is relevant or provided. |
| Dataset Splits | No | This paper is theoretical and does not involve experimental validation with dataset splits. |
| Hardware Specification | No | This paper is theoretical and does not describe experiments requiring hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | This paper is theoretical and does not mention any specific software dependencies with version numbers for experimental replication. |
| Experiment Setup | No | This paper is theoretical and does not include details about an experimental setup, hyperparameters, or training configurations. |