Paths to Equilibrium in Games

Authors: Bora Yongacoglu, Gurdal Arslan, Lacra Pavel, Serdar Yuksel

NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We answer this question in the affirmative for normal-form games. Our analysis reveals a counterintuitive insight that reward deteriorating strategic updates are key to driving play to equilibrium along a satisficing path. We prove this result by analytically constructing a satisficing path from an arbitrary initial strategy profile to a Nash equilibrium. This paper does not contain experiments.
Researcher Affiliation Academia Bora Yongacoglu Gürdal Arslan University of Toronto University of Hawaii at Manoa bora.yongacoglu@utoronto.ca gurdal@hawaii.edu Lacra Pavel Serdar Yüksel University of Toronto Queen s University pavel@control.toronto.edu yuksel@queensu.ca
Pseudocode No The paper discusses MARL algorithms but does not provide any pseudocode or clearly labeled algorithm blocks.
Open Source Code No The NeurIPS Paper Checklist states 'This paper does not contain experiments.' and 'The answer NA means that paper does not include experiments requiring code.' which implies no code is provided for the methodology.
Open Datasets No This paper is theoretical and does not involve the use of datasets for training or experimentation.
Dataset Splits No This paper is theoretical and does not involve the use of datasets or their validation splits.
Hardware Specification No This paper is theoretical and does not involve experiments requiring specific hardware specifications.
Software Dependencies No This paper is theoretical and does not involve experiments that would list specific software dependencies with version numbers.
Experiment Setup No This paper is theoretical and does not involve experiments requiring a specific experimental setup or hyperparameters.