A Unified Model of Direct and Indirect Reciprocity in Multichannel Games
Authors: Juan Shi, Zhaoheng Cao, Jinzhuo Liu, Chu Chen, Zhen Wang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The theoretical analysis above assumes a static scenario where players in the population adopt the same strategy. To further explore the evolutionary dynamics when players engage in social learning to adapt their strategies, we conduct two sets of evolutionary experiments to complement the theoretical analysis. The results of these experiments are consistent with the equilibrium predictions. |
| Researcher Affiliation | Academia | Juan Shi1, Zhaoheng Cao2, Jinzhuo Liu3,5,6, Chen Chu4,5*, Zhen Wang5,7* 1 School of Automation, Northwestern Polytechnical University 2 School of Computer Science, Northwestern Polytechnical University 3 School of Software, Yunnan University 4 School of Statistics and Mathematics, Yunnan University of Finance and Economics 5 School of Artificial Intelligence, OPtics and Electro Nics (i OPEN), Northwestern Polytechnical University 6 Engineering Research Center of Cyberspace, Yunnan University 7 School of Cybersecurity, Northwestern Polytechnical University EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods through narrative text and mathematical equations, but does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide a specific link to source code, nor does it contain an explicit statement about the release of code for the described methodology. |
| Open Datasets | No | The paper describes a theoretical model and conducts evolutionary experiments based on simulated game dynamics with defined parameters (e.g., n=50, b1=2, b2=1.2, c1=c2=1), rather than utilizing external, publicly available datasets that would require specific access information. |
| Dataset Splits | No | The paper conducts evolutionary experiments using simulations based on defined game parameters and agent interactions, and therefore does not involve traditional datasets with specified training, validation, or test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts, or detailed computer specifications) 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, that were used to replicate the experiments. |
| Experiment Setup | Yes | Figure 4 records the strategies adopted by all players. As predicted by the equilibrium results, players tend to adopt either a defection strategy (...). The histogram represents the distribution of strategies among the 500 most long-lived residents strategies. We continuously introduce 2 × 10^7 mutants. Parameter δ = 0.95, n = 50, b1 = 5, b2 = 3, c1 = c2 = 1. Figure 5: The evolutionary dynamics of direct and indirect reciprocity. The line represents the abundance of indirect information used in three different scenarios. n = 50, b1 = 5, b2 = 3, c1 = c2 = 1. |