  We present a methodology for evidence based design of cryptoeconomic systems,
and elucidate a real-world example of how this methodology was used in the
design of a blockchain network. This work provides a rare insight into the
application of Data Science and Stochastic Simulation and Modelling to Token
Engineering. We demonstrate how the described process has the ability to
uncover previously unexpected system level behaviors. Furthermore, it is
observed that the process itself creates opportunities for the discovery of new
knowledge and business understanding while developing the system from a high
level specification to one precise enough to be executed as a computational
model. Discovery of performance issues during design time can spare costly
emergency interventions that would be necessary if issues instead became
apparent in a production network. For this reason, network designers are
increasingly adopting evidence-based design practices, such as the one
described herein.
