A Successful Strategy for Multichannel Iterated Prisoner’s Dilemma
Authors: Zhen Wang, Zhaoheng Cao, Juan Shi, Peican Zhu, Shuyue Hu, Chen Chu
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical analyses of the limitations of multichannel WSLS and CIC, illustrating that they are vulnerable to long-lasting exploitation and can lose to multiple common strategies. Evolutionary experiments with two-strategy and three-strategy populations, demonstrating MCSUC s evolutionary advantage over nine other strategies, and its capability to promote and sustain cooperation in evolutionary populations. |
| Researcher Affiliation | Collaboration | 1School of Computer Science, Northwestern Polytechnical University 2School of Cybersecurity, Northwestern Polytechnical University 3School of Artificial Intelligence, OPtics and Electro Nics (i OPEN), Northwestern Polytechnical University 4School of Automation, Northwestern Polytechnical University 5Shanghai Artificial Intelligence Laboratory 6School of Statistics and Mathematics, Yunnan University of Finance and Economics |
| Pseudocode | No | The paper describes the proposed strategy in prose (Definition 1) but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | No | The paper conducts simulations of game theory strategies and defines game parameters and a payoff matrix. It does not refer to or provide access to an external, publicly available 'dataset' in the conventional sense of a collection of samples or instances. |
| Dataset Splits | No | The paper describes evolutionary experiments and simulations but does not specify training, validation, or test dataset splits in the manner typical for machine learning models, as it relies on simulation dynamics rather than partitioned datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | For each simulation, there are two steps. First, we obtain the average payoffs of two strategies in 1,000 simulation runs each lasting for 1,000,000 rounds. The payoff matrix is shown in Appendix Section 1. Then through a noisy survival of the fittest environment with a mutation rate 10%... One strategy is MCSUC with 1 = 2, 2 = 4... There are two cases of error rates: = 1% (solid line) and = 10% (dashed line). |