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A memory-based spatial evolutionary game with the dynamic interaction between learners and profiteers
Bin Pi1, Minyu Feng2, Liang-Jian Deng1
1School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China.
Chaos (Woodbury, N.Y.)
|June 7, 2024
Summary
This study introduces a memory-based spatial evolutionary game with dynamic interactions between learners and profiteers, revealing that these dynamics and memory mechanisms promote cooperation. Increased learning rates further enhance cooperative behaviors.
Area of Science:
- Evolutionary Game Theory
- Network Science
- Computational Social Science
Background:
- Spatial evolutionary games often overlook memory effects and dynamic population structures.
- Previous models typically focus on single-round interactions and static individual roles.
Purpose of the Study:
- To investigate the role of memory and dynamic interactions between distinct individual types (learners and profiteers) in spatial evolutionary games.
- To understand the emergence and maintenance of cooperation under these novel conditions.
Main Methods:
- Development of a memory-based spatial evolutionary game model incorporating dynamic transitions between profiteer and learner roles via a Markov process.
- Incorporation of memory mechanisms into individual payoff calculations.
- Extensive numerical simulations on various network structures to analyze evolutionary dynamics.
Main Results:
- Dynamic interactions between profiteers and learners significantly foster cooperation.
- Memory mechanisms facilitate the emergence of cooperative behaviors, particularly among profiteers.
- An increased learning rate among learners positively correlates with a higher prevalence of cooperators.
Conclusions:
- The proposed model provides a more realistic framework for studying cooperation by including memory and dynamic population structures.
- Findings highlight the importance of adaptive learning and memory in promoting cooperation in social dilemmas.
- The study deepens the understanding of the evolutionary mechanisms underlying cooperative behavior in complex networks.
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