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Cooperation in structured populations via coupled reputation and learning: A spatial evolutionary game approach
Shouwei Li1, Bo Peng1, Baochen Li2
1School of Management, Guilin University of Aerospace Technology, Guilin, Guangxi 541004, China.
Bio Systems
|October 24, 2025
Summary
This agent-based model shows how memory and learning help cooperation thrive in spatial evolutionary games, even against strong incentives for defection.
Area of Science:
- Evolutionary Game Theory
- Agent-Based Modeling
- Computational Social Science
Background:
- Cooperation is crucial in social systems but difficult to maintain against selfish strategies.
- Spatial structure and individual learning influence strategy dynamics in evolutionary games.
Purpose of the Study:
- To investigate cooperation dynamics in spatial evolutionary games.
- To explore the impact of memory-based reputation and heterogeneous adaptive learning on cooperation.
Main Methods:
- Developed an agent-based model on a lattice network.
- Agents update strategies using neighbors' cooperation history and payoff differences.
- Employed a modified Fermi rule with individual sensitivity parameters.
Main Results:
- The dual-layered mechanism sustains cooperation under strong defection incentives and limited interaction ranges.
- Memory length and learning heterogeneity jointly shape spatial cooperation patterns.
- Strategy diversity is influenced by the interplay of memory and learning.
Conclusions:
- Decentralized mechanisms integrating memory and learning can promote cooperation in structured populations.
- Findings have implications for understanding cooperation in evolutionary biology, distributed systems, and behavioral economics.
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