| Complex networks exist in nature and human society widely. To study complex networks has become one of advanced subjects of the greatest challenges in the fields of complex systems and complexity science. Based on abstracting a complex system as a network consisting of many interacting individuals, scientists in different fields working together try to exploit the profound mysteries of complex networks theoretically and empirically, and to reveal the essence and inherent regularity of interaction on human society and biological system, aiming to provide solid theoretical basis for constructing perfect network models. Nevertheless, the ultimate aim for studying compelx networks is to investigate how the network topology and dynamical mechanism affect the dynamics process, and to probe macroscopic phenomena in the system induced by microscopic interaction. According to the latest trends and development strategy at home and abroad, in this paper, we have done through going and painstaking research on evolutionary game dynamics and consensus dynamics based on naming game in complex networks, explored in greater depth some underlying dynamics mechanism in persisting and promoting cooperation, as well as in improving convergence efficiency for the consensus of the system. The main innovation points of this paper are as follows:1. The effect of HK clusterd scale-free networks on the evolution of cooperation, in the case of different initial distributions, is investigated. It is found that, on the one hand, cooperarion can be enhanced with the increasing clusting coefficeient when only the most connected nodes are occupied by cooperators initially. And this enhanced cooperation is robust with respect to the increasing number of initial cooperators. On the other hand, if cooperators just occupy the lowest-degree nodes at the beginning, then the higher the value of the clustering coefficient, the more unfavorable the environment of cooperators to survive for the increment of temptation to defect. In this case, there are a lot of cooperators in the beginning of evolution. Bsides, we investigate the Snowdrift game in BA scale-free networks and find that, the magnitude of the fitness degermines qualitatively the dissemination trend of cooperative behavior in complex networks.2. We investigate the effects of heterogeneous investment and distribution on the evolution of cooperation in the context of the public goods games. To do this, we develop a simple model in which each individual allocates differing funds to his direct neighbors based upon their difference in connectivity, because of the heterogeneity of real social ties. This difference is characterized by the weight of the link between paired individuals, with an adjustable parameter precisely controlling the heterogeneous level of ties. By numerical simulations, it is found that allocating both too much and too little funds to diverse neighbors can remarkably improve the cooperation level. However, there exists a worst mode of funds allocation leading to the most unfavorable cooperation induced by the moderate values of the parameter.3. The effect of memory on the evolution of cooperation on a square lattice is investigated. The fitness of individuals are characterized by two types of payoffs being obtained by acting as cooperators and defectors, respectively, both of which are the linear combination of the current payoffs and the cumulative historical payoffs. Simulation results show that cooperation is promoted by an increasing memory effect over a wide range of the multiplication factor. Defectors can just survive through forming narrower clusters to exploit cooperators more widely. For each decaying factor of historical payoffs, there exist two threshold values of the multiplication factor, below/above which cooperators/defectors would vanish completely from the system.4. We propose a coevolutionary version to investigate the naming game, a model recently introduced to describe how shared vocabulary can emerge and persist spontaneously in communication systems. We base our model on the fact that more popular names have more opportunities to be selected by agents and then spread in the population. A name’s popularity is concerned with its communication frequency, characterized by its weight coevolving with the name. A tunable parameter governs the influence of name weight. We implement thismodified version on both scale-free networks and small-world networks, in which interactions proceed between paired agents by means of the reverse naming game. It is found that there exists an optimal value of the parameter that induces the fastest convergence of the population. This illustration indicates that a moderately strong influence of evolving name weight favors the rapid achievement of final consensus, but very strong influences inhibit the convergence process. The rank-distribution of the final accumulated weights of names qualitatively explains this nontrivial phenomenon. Investigations of some pertinent quantities are also provided, including the time evolution of the number of different names and the success rate, as well as the total memory of agents for different parameter values, which are helpful for better understanding the coevolutionary dynamics. Finally, we explore the scaling behavior in the convergence time and conclude a smaller scaling parameter compared to the previous naming gamemodels.5. We propose a simple model to investigate the evolutionary dynamics of opinions on well-mixed populations.We assume that each individual has an inherent propensity to maintain his own word (opinion) about an object whereas other individuals would affect his decision when they communicate. On the one hand, individuals learn the opinion of another one with a probability pertaining to their propensities. On the other hand, the focal individual would adopt the word held by the majority in a randomly selected group. We have numerically explored how dynamical behavior evolves as a result of combination of these two competing update patterns. A parameter governs the time scale ratio at which the two update patterns separately progress.We find that an increasing tendency to adopt the opinion held by the majority results in a rapid extinction of most opinions, thus more easily induces the system to a global consensus. Large initial probabilities denoting propensity are found to be unfavorable for the achievement of the consensus. Interestingly, simulation results indicate that the convergence time is negligibly affected by the number of initial distinct words when this number exceeds a certain value. Results from our model may offer an insight into better understanding the intricate dynamics of opinions. |
