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.
Computing Optimal Equilibria in Repeated Games with Restarts
Authors: Ratip Emin Berker, Vincent Conitzer
IJCAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 19,203 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,902 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 6 Experiments We present the semi-log plots for the runtimes of Algorithms 1-3 in Figure 1. Given the number of actions n and a positive integer Maximum Payoff of Deviation (MPD), we generated a game by (for each action j 2 [n]) uniformly choosing p(j) from {i 2 Z : 0 i 30} and uniformly choosing p (j) from {i 2 Z : p(j) i MPD}.3 As expected, the runtimes of all algorithms increase with increasing n, and the runtime of FPTAS increases with decreasing |
| Researcher Affiliation | Academia | Ratip Emin Berker and Vincent Conitzer Foundations of Cooperative AI Lab (FOCAL), Computer Science Department, Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1 Dynamic Program for Opt Rep, Algorithm 2 Integer Linear Program for Opt Rep, Algorithm 3 FPTAS for Opt Rep |
| Open Source Code | No | No mention of open-source code release or repository links. |
| Open Datasets | No | Given the number of actions n and a positive integer Maximum Payoff of Deviation (MPD), we generated a game by (for each action j 2 [n]) uniformly choosing p(j) from {i 2 Z : 0 i 30} and uniformly choosing p (j) from {i 2 Z : p(j) i MPD}. |
| Dataset Splits | No | Given the number of actions n and a positive integer Maximum Payoff of Deviation (MPD), we generated a game by (for each action j 2 [n]) uniformly choosing p(j) from {i 2 Z : 0 i 30} and uniformly choosing p (j) from {i 2 Z : p(j) i MPD}. |
| Hardware Specification | No | No mention of specific GPU/CPU models, processor types, or memory amounts used for experiments. |
| Software Dependencies | No | The paper discusses algorithmic approaches (Dynamic Program, ILP, FPTAS) but does not provide specific software dependencies or version numbers used for their implementation. |
| Experiment Setup | Yes | Given the number of actions n and a positive integer Maximum Payoff of Deviation (MPD), we generated a game by (for each action j 2 [n]) uniformly choosing p(j) from {i 2 Z : 0 i 30} and uniformly choosing p (j) from {i 2 Z : p(j) i MPD}. Each data point is averaged over 5000 trials. |