Congestion Games with Polytopal Strategy Spaces

Authors: Hau Chan, Albert Xin Jiang

IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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 {hchan,xjiang}@trinity.edu
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.