Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Game Redesign in No-regret Game Playing
Authors: Yuzhe Ma, Young Wu, Xiaojin Zhu
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations on four classic games confirm the effectiveness of our proposed redesign algorithms. |
| Researcher Affiliation | Academia | Yuzhe Ma , Young Wu , Xiaojin Zhu University of Wisconsin Madison EMAIL |
| Pseudocode | Yes | Algorithm 1 Interior Design; Algorithm 2 Boundary Design; Algorithm 3 Discrete Design |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper defines and simulates classic game theory scenarios (e.g., Volunteer's Dilemma, RPS) within the paper itself or by reference to well-known theoretical constructs. It does not use or link to external, publicly available datasets in the traditional sense that require concrete access information. |
| Dataset Splits | No | The paper describes running simulations for a number of rounds (T) and analyzing the outcomes, rather than using a train/validation/test split of a dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU/CPU models or cloud resources. |
| Software Dependencies | No | The paper mentions using 'EXP3.P [Bubeck and Cesa-Bianchi, 2012]' as the no-regret learner but does not specify a version number for it or for any other key software components or libraries used. |
| Experiment Setup | Yes | The margin parameter is ρ = 1. We ran Algorithm 2 for ϵ = 0.1, 0.2, 0.3, 0.4. For each ϵ we simulated game play for T = 104, 105, 106 and 107. |