Evidence Based Decision Making in Blockchain Economic Systems: From Theory to Practice
This work addresses the challenge of designing reliable and efficient blockchain networks for network designers, offering a practical approach to improve system robustness and reduce risks, though it appears incremental in applying existing data science and simulation techniques to token engineering.
The paper tackles the problem of designing blockchain economic systems by introducing a methodology for evidence-based design, demonstrating its application in a real-world blockchain network to uncover unexpected system behaviors and performance issues during design time, thereby avoiding costly emergency interventions.
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.