The Opinion Consensus And Dynamics Of Human Innovative Behaviors On Social Networks

Dynamics research on complex networks usually has a realistic background, and is of profound practical significance in understanding, controlling, and optimizing the real system. Compared to other complex system, the dynamics mechanism of social system formed by human beings is much more complex. In present paper, the opinion dynamics and the dynamics of human innovation behavior on social network by agent-based method is studied.(1)We propose an opinion dynamics model to study the effects of heterogeneous influence of individuals on the global consensus. Each individual is assigned a weight of influence according to its degree. A tunable parameter a is introduced to govern the weight of influence. Interestingly, it is found that there exists an optimal value of α, resulting in the shortest consensus time for scale-free networks, ER random networks and small-world networks. To explain this phenomenon, we study the probability of an individual’s initial opinion the same as the final opinion as a function of degree, the evolution of the number of opinion clusters, and find that the medium α balance the cohesion inside the cluster and the competition between clusters, leading to fastest convergence speed. Furthermore, the relationship between average consensus time and the network size obeys the power function controlled by a. Our results are helpful for understanding the role of heterogeneous of individual property in the opinion dynamics.(2) How to promote the innovative activities is an important problem for modern society, In this paper, combining the evolutionary games with information spreading, we propose a lattice model to investigate dynamics of human innovative behaviors based on benefit-driven assumption. We consider the human innovative behaviors in the broad term, which is the behaviors original and followed by more participants. Simulations show several properties in agreement with peoples’ daily cognition on innovative behaviors, such as slow diffusion of innovative behaviors, gathering of innovative strategy on "innovative centers", and quasi-localized dynamics. Furthermore, our model also emerges rich non-Poisson properties in the temporal-spacial patterns of the innovative status, including the scaling law in the interval time of innovation releases and the bimodal distributions on the spreading range of innovations, which would be universal in human innovative behaviors. Our model provides a basic framework on the study of the issues relevant to the evolution of human innovative behaviors and the promotion measurement of innovative activities.(3)Based on the above results, we apply the BA network on the dynamics to study the evolution of innovative behavior on heterogeneous networks. We found that the relative costs a of innovation activities strongly influence on the innovative behavior’s evolution. The relationship between innovators and the relationship between innovators and followers are partly competition and partly dependent, leading to special properties different from other evolutionary games:large average degree (k) of the network can promote the level of innovative behavior, but also reduces the system affordability of cost α. The quasi-localized dynamics still exist and the system emerges rich non-Poisson properties in the temporal-spacial patterns of the innovative status. Furthermore, the individuals with different degrees behave heterogeneously with different strategic choice tendencies.