Research On Evolutionary Games On Complex Networks Based On Switching Probability And Extensive Cooperation Strategy

The smooth operation of natural,social or economic systems requires the altruistic collaboration of the group members.From the Darwinist perspective,the emergence and maintenance of cooperative behaviors among selfish individuals is puzzling,when free riding is the advantageous choice for agents who pursuit the maximization of their interest.Until now,rich and diverse literatures from different disciplines have presented findings for this puzzle.Among these,a most-often used framework to describe cooperative dilemma is the evolutionary game theory.In traditional settings,an ideal hypothesis that individuals can feasibly obtain related partners' payoffs for strategy updating is often adopted.However,in real social systems,acquiring the accurate payoffs of partners at each round of interaction may be not easy.We bypass this difficulty and encode the payoffs into the willingness of any individual shift from her current strategy to the competing one,and the switching probabilities are wholly independent of payoffs.Moreover,in common setups of evolutionary games,a standard assumption for the payoff matrix of a game play is that their opponents(cooperators or defectors)will gain benefits since an opponent can always reap the benefits of cooperation done by its cooperative partners.In real-world situations,the altruistic behaviors of cooperators not only directly enhance the benefits of their game opponents,but also indirectly produce beneficial influences on other related members in their social surroundings.We propose extensive cooperators to extend cooperative effects to their social groups.The main researches of this thesis are listed as follows:(1)We study the evolutionary dynamics of strategies in finite populations which are homogeneous and well mixed by means of the pairwise comparison process,the core of which is the proposed switching probability.We explore the impact of the switching probability on the fixation probability and derive a simple formula which determines the fixation probability.And we investigate the influences of model parameters on the fixation of strategies in the framework of three concrete game models: prisoner's dilemma,snowdrift game and stag-hunt game,which effectively portray the characteristics of cooperative dilemmas in real social systems.(2)By the aid of the stability theory,we gain hints about driving the multi-agent system to evolve into the expected state of pure strategy in the framework of evolutionary game theory and complex connected networks.Last we study the influence of game model parameters and selection intensities on the fraction of cooperators in the framework of three concrete game models.(3)We provide results for the evolutionary game dynamics driven by the switching probability in a three-strategy game model,played by the fully-connected populations.We also derive general results that characterize the interaction of the three strategies: coexistence of multiple strategies or domination by some strategy.Furthermore,considering the fact that we often seek advice from more than one person in real life,we get the real switching probabilities and the corresponding evolutionary game dynamics of referring to several neighbors.(4)Analysis on the expanded model based on the extensive cooperation strategy reveals that the larger punishment on defectors favors the evolutionary emergence and subsequent stability of cooperation in a population.Moreover,the difference resulted from changing the punishment increases as selection intensity grows.Considering the spatial distribution of the real world,we extend our framework to spatial populations where every agent only links to a limited number of neighbors.We find that the conclusion above is true for both homogeneous and heterogeneous populations in three concrete game models.