The Emergence of Knowledge Network in a Turbulent Problem-Solving Environment

We study the problem of knowledge network emerging in a turbulent environment where individual partner selection behavior and interaction structures coevolve. The objective is to explore the evolving architecture of problem-solving networks and how the co-evolution of knowledge network of actors´ knowledge searching behavior affects the system-level performance. Detailed examination shows that the emergent network evolve from several components to a one large component and the density of knowledge network become larger and larger. Turbulent environment will lead to the drop in system-level performance. Particularly, in a turbulent environment which each decision stay the same with a large possibility, Knowledge network provides the actors the speedy improvement and diverse search which leads to the possibility that actors will find the high-quality solution all over the population. In contrast, when environment undergoes a frequent change, which large dimensions of choice configuration have been changed, the system-level performance drop dramatically. We also notice that system-level performance fluctuate slightly after several periods under the significant changeable circumstance, which suggests that knowledge network is more useful to resist significant than frequent turbulence.