Emergence of Punishment in Social Dilemma with Environmental Feedback
Authors: Zhen Wang, Zhao Song, Chen Shen, Shuyue Hu
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analyze the co-evolution of punishment and cooperation, and derive conditions for their co-presence, co-dominance and co-extinction. Moreover, we show that the system can exhibit bistability as well as cyclic dynamics. Our findings provide a new explanation for the emergence of punishment. On the other hand, our results also alert the need for careful design of implementing punishment in multi-agent systems, as the resulting evolutionary dynamics can be somewhat complex. Last but not least, we corroborate our theoretical findings with numerical simulations on finite populations that evolve according to the Fermi process. |
| Researcher Affiliation | Academia | 1School of Mechanical Engineering, Northwestern Polytechnical University 2School of Artifcial Intelligence, OPtics and Electro Nics (i OPEN), Northwestern Polytechnical University 3Faculty of Engineering Sciences, Kyushu University 4Shanghai Artifcial Intelligence Laboratory |
| Pseudocode | No | The paper presents mathematical equations and descriptions of the model and dynamics, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The numerical simulations on finite populations and detailed proofs of our theoretical claims are presented in the supplementary1 due to the lack of space. 1https://github.com/yt-songz/AAAI2023SI |
| Open Datasets | No | The paper describes a theoretical model and numerical simulations of that model, but it does not mention the use of any specific, named, and publicly available dataset with concrete access information (link, DOI, or formal citation). |
| Dataset Splits | No | The paper describes numerical simulations of a theoretical model and does not specify training, validation, or test dataset splits in the context of empirical data. |
| Hardware Specification | No | The paper discusses theoretical analysis and numerical simulations, but does not provide any specific details about the hardware used for these simulations (e.g., GPU/CPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper describes a theoretical model and numerical simulations but does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | We visualize the phase portrait with different values of δD α given δC = 0. We gradually increase the value of δD α from 0.02 to 5. The trajectories that start with the same initial state ρC = 0.9,ρP = 0.2 are marked in red. Panel (a) depicts a phase portrait given δC = 0.2 and δD α = 0.2. |