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..
Nash Equilibria in Concurrent Games with Lexicographic Preferences
Authors: Julian Gutierrez, Aniello Murano, Giuseppe Perelli, Sasha Rubin, Michael Wooldridge
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study concurrent games with finite-memory strategies where players are given a B uchi and a mean-payoff objective, which are related by a lexicographic order: a player first prefers to satisfy its B uchi objective, and then prefers to minimise costs, which are given by a mean-payoff function. In particular, we show that deciding the existence of a strict Nash equilibrium in such games is decidable, even if players deviations are implemented as infinite memory strategies. |
| Researcher Affiliation | Academia | Julian Gutierrez1, Aniello Murano2, Giuseppe Perelli1, Sasha Rubin2, Michael Wooldridge1 1University of Oxford 2Universit a degli Studi di Napoli Federico II |
| Pseudocode | No | The paper describes algorithms and mathematical constructions, such as the linear program (LP) in Section 3.3, but it does not present any formal pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper does not contain any statement about making source code for their methodology publicly available, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or evaluate on any datasets, therefore no dataset access information for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments, therefore no training, validation, or test data splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments, therefore no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments that would require specific software dependencies with version numbers. While it mentions linear programming, it does not specify a particular solver or its version. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments, therefore no experimental setup details such as hyperparameters or training configurations are provided. |