Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Multi-Leader Congestion Games with an Adversary
Authors: Tobias Harks, Mona Henle, Max Klimm, Jannik Matuschke, Anja Schedel5068-5075
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,882 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,490 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 | 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. |