Abstract
Social reward, as a significant mechanism explaining the evolution of cooperation, has attracted great attention both theoretically and experimentally. In this paper, we study the evolution of cooperation by proposing a reward model in network population, where a third strategy, reward, as an independent yet particular type of cooperation is introduced in 2-person evolutionary games. Specifically, a new kind of role corresponding to reward strategy, reward agents, is defined, which is aimed at increasing the income of cooperators by applying to them a social reward. Results from numerical simulations show that consideration of social reward greatly promotes the evolution of cooperation, which is confirmed for different network topologies and two evolutionary games. Moreover, we explore the microscopic mechanisms for the promotion of cooperation in the three-strategy model. As expected, the reward agents play a vital role in the formation of cooperative clusters, thus resisting the aggression of defectors. Our research might provide valuable insights into further exploring the nature of cooperation in the real world.
Highlights
Irrespective of which game applies, agents can choose either to cooperate or to defect in the procedure of the game
A third strategy, reward, as an independent yet particular kind of cooperation strategy is introduced in the traditional prisoner’s dilemma game (PDG) and snowdrift game (SDG) models to explore the influence of social reward on the emergence of cooperative behavior
We have explored the impact of social reward on the evolution of cooperation in prisoners’ dilemma game and snowdrift game
Summary
Irrespective of which game applies, agents can choose either to cooperate or to defect in the procedure of the game. In the PDG, the payoffs must be ordered as T >R >P >S so that the defection is the best strategy irrespective of the opponent’s decision[22,23,24,25] Both in the SDG and the SHG, players interact in a similar way, but the payoff ranking is T >R >S >P (R >T >P >S) for the SDG (SHG). Many realistic scenarios are introduced into evolutionary games such as tit-for-tat, win-stay and lose-shift, memory effects, age structure, and different teaching capabilities[40,41,42,43,44] There is another situation of particular relevance that has received a lot of attention[45,46,47,48,49,50]. We introduce a third strategy (reward) in the traditional PDG and SDG and study how these defined ‘reward agents’ affect the evolution of cooperation in several topological settings
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