Designing evolvable systems in a framework of robust, resilient and sustainable engineering analysis

“Evolvability” is a concept normally associated with biology or ecology, but recent work on control of interdependent critical infrastructures reveals that network informatics systems can be designed to enable artificial, human systems to “evolve”. To explicate this finding, we draw on an analogy between disruptive behavior and stable variation in the history of science and the adaptive patterns of robustness and resilience in engineered systems. We present a definition of an evolvable system in the context of a model of robust, resilient and sustainable systems. Our review of this context and standard definitions indicates that many analysts in engineering (as well as in biology and ecology) do not differentiate Resilience from Robustness. Neither do they differentiate overall dependable system adaptability from a multi-phase process that includes graceful degradation and time-constrained recovery, restabilization, and prevention of catastrophic failure.We analyze how systemic Robustness, Resilience, and Sustainability are related to Evolvability. Our analysis emphasizes the importance of Resilience as an adaptive capability that integrates Sustainability and Robustness to achieve Evolvability.This conceptual framework is used to discuss nine engineering principles that should frame systems thinking about developing evolvable systems. These principles are derived from Kevin Kelly’s book: Out of Control, which describes living and artificial self-sustaining systems. Kelly’s last chapter, “The Nine Laws of God,” distills nine principles that govern all life-like systems. We discuss how these principles could be applied to engineering evolvability in artificial systems. This discussion is motivated by a wide range of practical problems in engineered artificial systems. Our goal is to analyze a few examples of system designs across engineering disciplines to explicate a common framework for designing and testing artificial systems. This framework highlights managing increasing complexity, intentional evolution, and resistance to disruptive events. From this perspective, we envision a more imaginative and time-sensitive appreciation of the evolution and operation of “reliable” artificial systems. We conclude with a short discussion of two hypothetical examples of engineering evolvable systems in network-centric communications using Error Resilient Data Fusion (ERDF) and cognitive radio.