Network Science addresses a topic popular in the agile community but addressed with little or no mathematical foundation.

The book describes topics in network and the complexity of network from physics to computer science, engineering, economics and the social sciences, introduces network science to an interdisciplinary audience.

From the origins of the six degrees of separation to explaining why networks are robust to random failures (anti-fragile), how viruses like Ebola and H1N1 spread, and why it is that our friends have more friends than we do.

The mathematical and their derivations are included, enabling use at a range of levels. Extensive online resources, including films and software for network analysis, make this a multifaceted companion for anyone with an interest in network science.

When you hear about *compexity* and frameworks for complexity from Cynefin to complex Bayesian systems, this is a book that connects them all.

The Cynefin framework is one starting point for decision making contexts to assess the processes of modeling and analysis in statistical, risk and decision analysis. But there are others for example "Towards the integration of system modelling with scenario planning to support strategy: the case of the UK energy industry,"

So once again, when some conjecture is made about a solution to a complex problem in Project Management, Software Development, especially Agile, and there are no references or *testable* principles to support the conjecture - it's a good sign it's just a personal anecdote and probably of little value outside the domain of the person making the conjecture.