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 [1].
Congestion Games with Polytopal Strategy Spaces
Authors: Hau Chan, Albert Xin Jiang
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we study congestion games in which each player s strategy space can be described compactly using a set of linear constraints. For instance, network congestion games naturally fall into this subclass as each player s strategy can be described by a set of flow constraints. We show that we can represent any mixed strategy compactly using marginals which specify the probability of using each resource. As a consequence, the expected utilities and the best responses can be computed in polynomial time. We reduce the problem of computing a best/worst symmetric approximate mixedstrategy Nash equilibrium in symmetric congestion games to a constraint optimization problem on a graph formed by the resources and the strategy constraints. As a result, we present a fully polynomial time approximation scheme (FPTAS) for this problem when the graph has bounded treewidth. |
| Researcher Affiliation | Academia | Hau Chan Albert Xin Jiang Department of Computer Science Trinity University, San Antonio, TX 78212, USA EMAIL |
| Pseudocode | No | The paper describes an algorithm (message passing) in detail, including steps and mathematical formulations, but it is presented in prose and equations rather than a formally structured pseudocode block or algorithm listing with typical pseudocode conventions. |
| Open Source Code | No | The paper does not provide any statements about releasing source code or links to code repositories. |
| Open Datasets | No | This paper is theoretical and does not involve training data or empirical experiments. |
| Dataset Splits | No | This paper is theoretical and does not involve validation data or empirical experiments. |
| Hardware Specification | No | This paper is theoretical and does not involve empirical experiments, therefore no hardware specifications are provided. |
| Software Dependencies | No | This paper is theoretical and does not involve empirical experiments, therefore no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | This paper is theoretical and does not involve empirical experiments, therefore no experimental setup details like hyperparameters or training settings are provided. |