Heterogeneity Playing Key Role: Modeling and Analyzing the Dynamics of Incentive Mechanisms in Autonomous Networks

摘要

Heterogeneities (heterogeneous characteristics) are intrinsic in dynamic and autonomous networks, and may be caused by the following factors: finite nodes, structured network graph, mutation of node's strategy and topological view, and dynamic linking, and so on. However, few works systematically investigate the effect of the intrinsic heterogeneities on the evolutionary dynamics of incentive mechanisms in autonomous networks. In this article, we thoroughly discuss this interesting problem. Specifically, this article respectively models the pairwise interaction between peers as PD (prisoner's dilemma)-like game and multiple peers' interactions as public-goods game, proposes a general analytical framework for dynamics in evolutionary game theory (EGT)-based incentive mechanisms, and draws the following conclusions. First, for explicit incentive mechanisms, due to heterogeneity, it is impossible to get the static equilibrium of absolutely-full-cooperation (or state that provides service to the networks—so-called reciprocation), but, on the other hand, heterogeneity can facilitate reciprocation evolution, and drive the whole system into almost-full-reciprocation state, that is, most of the system time would be occupied by the full reciprocation state. Second, even without any explicit incentive mechanisms, simultaneous coevolution between dynamic linking and peers' rational strategies can not only facilitate the cooperation evolution, but drive the network structure into the desirable small-world structure. The philosophical implication of our work is that simplicity and homogeneity are too idealized for incentive mechanisms in autonomous networks—diversity and heterogeneity are intrinsic for any incentive mechanism that is compatible with the essence of our real society. Diversity is everywhere.