Cooperation in structured populations via coupled reputation and learning: A spatial evolutionary game approach.
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| Názov: | Cooperation in structured populations via coupled reputation and learning: A spatial evolutionary game approach. |
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| Autori: | Li S; School of Management, Guilin University of Aerospace Technology, Guilin, Guangxi 541004, China., Peng B; School of Management, Guilin University of Aerospace Technology, Guilin, Guangxi 541004, China. Electronic address: peng.bo@guat.edu.cn., Li B; School of Aerospace Engineering, Guilin University of Aerospace Technology, Guilin, 541004, Guangxi, China., Shi Y; School of Management, Guilin University of Aerospace Technology, Guilin, Guangxi 541004, China. |
| Zdroj: | Bio Systems [Biosystems] 2025 Dec; Vol. 258, pp. 105630. Date of Electronic Publication: 2025 Oct 22. |
| Spôsob vydávania: | Journal Article |
| Jazyk: | English |
| Informácie o časopise: | Publisher: Elsevier Science Ireland Country of Publication: Ireland NLM ID: 0430773 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-8324 (Electronic) Linking ISSN: 03032647 NLM ISO Abbreviation: Biosystems Subsets: MEDLINE |
| Imprint Name(s): | Publication: Limerick : Elsevier Science Ireland Original Publication: Amsterdam, North-Holland Pub. Co. |
| Výrazy zo slovníka MeSH: | Game Theory* , Cooperative Behavior* , Biological Evolution* , Learning*/physiology, Humans ; Computer Simulation |
| Abstrakt: | Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This study presents an agent-based model to investigate cooperation dynamics in spatial evolutionary games by integrating memory-based reputation tracking with heterogeneous adaptive learning. Agents interact on a lattice network and update their strategies based on both neighbors' historical cooperation rates and payoff differences, governed by a modified Fermi rule with individual sensitivity parameters. Simulation results demonstrate that this dual-layered mechanism sustains cooperation even under strong defection incentives and limited interaction ranges. The model also reveals how memory length and learning heterogeneity jointly influence spatial cooperation patterns and strategy diversity. These findings offer new insights into decentralized mechanisms that promote cooperation in structured populations, with implications for evolutionary biology, distributed systems, and behavioral economics. (Copyright © 2025 Elsevier B.V. All rights reserved.) |
| Contributed Indexing: | Keywords: Adaptive learning; Agent-based modeling; Cooperation dynamics; Memory-based reputation; Spatial evolutionary games |
| Entry Date(s): | Date Created: 20251024 Date Completed: 20251116 Latest Revision: 20251116 |
| Update Code: | 20251117 |
| DOI: | 10.1016/j.biosystems.2025.105630 |
| PMID: | 41135606 |
| Databáza: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstrakt: | Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />This study presents an agent-based model to investigate cooperation dynamics in spatial evolutionary games by integrating memory-based reputation tracking with heterogeneous adaptive learning. Agents interact on a lattice network and update their strategies based on both neighbors' historical cooperation rates and payoff differences, governed by a modified Fermi rule with individual sensitivity parameters. Simulation results demonstrate that this dual-layered mechanism sustains cooperation even under strong defection incentives and limited interaction ranges. The model also reveals how memory length and learning heterogeneity jointly influence spatial cooperation patterns and strategy diversity. These findings offer new insights into decentralized mechanisms that promote cooperation in structured populations, with implications for evolutionary biology, distributed systems, and behavioral economics.<br /> (Copyright © 2025 Elsevier B.V. All rights reserved.) |
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| ISSN: | 1872-8324 |
| DOI: | 10.1016/j.biosystems.2025.105630 |