Zhang, Yuji and Feng, Minyu and Kurths, Jürgen and Szolnoki, Attila (2025) Evolutionary Dynamics Based on Reputation in Networked Populations with Game Transitions. IEEE Transactions on Signal and Information Processing over Networks. ISSN 2373-776X (In Press)
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Abstract
Abstract—The environment undergoes perpetual changes that are influenced by a combination of endogenous and exogenous factors. Consequently, it exerts a substantial influence on an individual’s physical and psychological state, directly or indirectly affecting the evolutionary dynamics of a population described by a network, which in turn can also alter the environment. Furthermore, the evolution of strategies, shaped by reputation, can diverge due to variations in multiple factors. To explore the potential consequences of the mentioned situations, this paper studies how game and reputation dynamics alter the evolution of cooperation. Concretely, game transitions are determined by individuals’ behaviors and external uncontrollable factors. The cooperation level of its neighbors reflects individuals’ reputation, and further, a general fitness function regarding payoff and reputation is provided. Within the context of the donation game, we investigate the relevant outcomes associated with the aforementioned evolutionary process, considering various topologies for distinct interactions. Additionally, a biased mutation is introduced to gain a deeper insight into the strategy evolution. We detect a substantial increase in the cooperation level through intensive simulations, and some important phenomena are observed, e.g., the unilateral increase of the value of prosocial behavior has limited promotion in cooperative behavior in square-lattice networks.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Evolutionary game theory, networked populations, cooperative behavior, game transitions, reputation, complex systems |
| Subjects: | Q Science / természettudomány > QA Mathematics / matematika |
| SWORD Depositor: | MTMT SWORD |
| Depositing User: | MTMT SWORD |
| Date Deposited: | 10 Dec 2025 13:46 |
| Last Modified: | 10 Dec 2025 13:46 |
| URI: | https://real.mtak.hu/id/eprint/230405 |
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