  The dynamics of protection processes has been a fundamental challenge in
systemic risk analysis. The conceptual principle and methodological techniques
behind the mechanisms involved [in such dynamics] have been harder to grasp
than researchers understood them to be. In this paper, we show how to construct
a large variety of behaviors by applying a simple algorithm to networked
agents, which could, conceivably, offer a straightforward way out of the
complexity. The model starts with the probability that systemic risk spreads.
Even in a very random social structure, the propagation of risk is guaranteed
by an arbitrary network property of a set of elements. Despite intensive
systemic risk, the potential of the absence of failure could also be driven
when there has been a strong investment in protection through a heuristically
evolved protection level. It is very interesting to discover that many
applications are still seeking the mechanisms through which networked
individuals build many of these protection process or mechanisms based on
fitness due to evolutionary drift. Our implementation still needs to be
polished against what happens in the real world, but in general, the approach
could be useful for researchers and those who need to use protection dynamics
to guard against systemic risk under intrinsic randomness in artificial
circumstances.
