Multi-Leader Congestion Games with an Adversary

Authors: Tobias Harks, Mona Henle, Max Klimm, Jannik Matuschke, Anja Schedel5068-5075

AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical As our first main result, we show that the existence of a K-approximate equilibrium can always be guaranteed, where K 1.1974 is the unique solution of a cubic polynomial equation. To this end, we give a polynomial time combinatorial algorithm which computes a K-approximate equilibrium. The factor K is tight, meaning that there is an instance that does not admit an α-approximate equilibrium for any α < K.
Researcher Affiliation Academia Tobias Harks1, Mona Henle2, Max Klimm3, Jannik Matuschke4, Anja Schedel1 1 University of Augsburg, 86159 Augsburg, Germany 2 University of Applied Sciences Augsburg, 86161 Augsburg, Germany 3 TU Berlin, 10623 Berlin, Germany 4 KU Leuven, 3000 Leuven, Belgium
Pseudocode Yes Algorithm 1: Computation of an α-approximate PNE. Input: Player set N = [n], resource set R = {r1, . . . , rm}, resource cost coefficients 0 a1 am, and α 1. Output: α-approximate pure Nash equilibrium x.
Open Source Code No The paper does not provide an explicit statement about the release of source code or a link to a code repository for the methodology described.
Open Datasets No The paper is theoretical and does not use or refer to any publicly available or open datasets for training or evaluation in the empirical sense.
Dataset Splits No The paper is theoretical and does not describe empirical experiments, so there is no mention of training/validation/test dataset splits.
Hardware Specification No The paper is theoretical and does not describe empirical experiments, thus no specific hardware details used for running experiments are provided.
Software Dependencies No The paper is theoretical and does not describe empirical experiments, thus no specific ancillary software details with version numbers are provided.
Experiment Setup No The paper is theoretical and does not describe empirical experiments, thus no specific experimental setup details, such as hyperparameters or training configurations, are provided.