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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Zero-sum Polymatrix Markov Games: Equilibrium Collapse and Efficient Computation of Nash Equilibria
Authors: Fivos Kalogiannis, Ioannis Panageas
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 27,144 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,872 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The proof relies on the fact that zero-sum polymatrix Markov games with a switching controller have the following important property: the marginals of a coarse-correlated equilibrium constitute a Nash equilibrium (see Section 3.2). We refer to this phenomenon as equilibrium collapse. This property was already known for zero-sum polymatrix normal-form games by Cai et al. (2016) and our results generalize the aforementioned work for Markov games. As a corollary, we get that any algorithm in the literature that guarantees convergence to approximate coarse-correlated equilibria Markovian policies e.g., (Daskalakis et al., 2022) can be used to get approximate Nash equilibria. |
| Researcher Affiliation | Academia | Fivos Kalogiannis Department of Computer Science University of California, Irvine Irvine, CA EMAIL Ioannis Panageas Department of Computer Science University of California, Irvine Irvine, CA EMAIL |
| Pseudocode | Yes | Algorithm 1 SPo CMAR (Daskalakis et al., 2022) and Algorithm 2 Est Visitation are provided in Appendix D. |
| Open Source Code | No | The paper refers to algorithms from other papers but does not state that the authors are providing open-source code for their own work or methodology. |
| Open Datasets | No | The paper is theoretical and does not involve training models on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithms. It does not mention specific software dependencies or versions for implementation. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, therefore, no experimental setup details like hyperparameters or training settings are provided. |