Computing Optimal Equilibria in Repeated Games with Restarts
Authors: Ratip Emin Berker, Vincent Conitzer
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 {rberker, conitzer}@cs.cmu.edu |
| 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. |