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..
Small Nash Equilibrium Certificates in Very Large Games
Authors: Brian Zhang, Tuomas Sandholm
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments showed that many large or even infinite games have small certificates, allowing us to find equilibria while exploring a vanishingly small portion of the game. ... 7 Experiments We conducted experiments using the algorithm in Section 6 on the following common zero-sum benchmark games. ... Results of experiments can be found in Table 1. |
| Researcher Affiliation | Collaboration | Brian Hu Zhang Computer Science Department Carnegie Mellon University EMAIL Tuomas Sandholm Computer Science Department, CMU Strategic Machine, Inc. Strategy Robot, Inc. Optimized Markets, Inc. EMAIL |
| Pseudocode | Yes | Algorithm 6.7 Finding a certificate in a two-player zero-sum game ... Algorithm 6.9 CORRECT(I): Correcting a strategy in the case of infinite reward |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | (1) A zero-sum variant of the search game [4]. ... (2) k-rank Goofspiel. ... (3) k-rank limit Leduc poker. ... [4] refers to Branislav Bošanský and Jiří Čermák. Sequence-form algorithm for computing Stackelberg equilibria in extensive-form games. In Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015. |
| Dataset Splits | No | The paper discusses game theory and equilibrium finding rather than machine learning models with train/validation/test dataset splits. It does not provide specific details on how data was split for training, validation, or testing. |
| Hardware Specification | No | The paper states 'For the LP solver, we used Gurobi v9.0.0 [15],' but it does not specify any hardware components (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | Yes | For the LP solver, we used Gurobi v9.0.0 [15]. |
| Experiment Setup | No | The paper states, 'We computed 0-certificates in all cases. For the LP solver, we used Gurobi v9.0.0 [15],' and describes the games used. However, it does not provide specific hyperparameter values, detailed training configurations, or other system-level settings for reproducibility beyond the choice of solver. |