To Govern the Future We Need Anticipation First: No Anticipation No System Antifragility

Current economic and social problems are multiscale-order deficiencies, which cannot be fixed by the usual, traditional hierarchical approach alone, by doing what we do better or more intensely, but rather by changing the way we do. The major crises of the last ten years (financial, ecological, social, medical, and even moral) illustrate the urgent need for an anticipatory perspective. Furthermore, as the experiences in the latest fifty years have shown, unpredictable changes can be very disorienting at enterprise level. They need to be handled, as opportunities, as positively as possible (Taleb, 2015). In describing the living, regardless of its complexity, from monocell to the whole human being, descriptions based on the deterministic understanding of the world and the corresponding reductionist model fail to capture the defining characteristic of life: the ability to anticipate. In a continuously changing operational environment, even if operational parameters cannot be closely pre-defined at system planning and design level, we need to be able to plan and to design antifragility self-organizing, self-regulating and self-adapting system quite easily anyway. Today, operational and environmental conditions are continuously changing at an increasing rate. While the processing power doubles every 1.8 years and the amount of data doubles every 1.2 years, the complexity of networked systems is growing even faster. Attempts to optimize hierarchical systems in the traditional top-down way will be less and less effective, and cannot be done in real time (Fiorini, 2016). The logical answer is to use distributed (self-) control, i.e. bottom-up self-regulating systems. Advanced Cybernetics (i.e. extended system theory) and Complexity Theory tell us that it is actually feasible to create resilient social and economic order by means of self-organization, self-regulation, and self-governance (Ostrom, 1990; 2010). Nevertheless, to achieve self-organization, self-regulation in a competitive, arbitrary-scalable system reference framework, we need application resilience and antifragility at system level first. But with no anticipation, we have no system learning. In turn, with no learning, we have no system antifragility. In fact, current human made application and system are quite vulnerable and fragile to unexpected perturbation because Statistics by itself can fool you, unfortunately (Taleb & Douady, 2015). What Nassim Taleb has identified and calls "antifragility" is that category of things that not only gain from chaos but need it in order to survive and flourish and proposes that systems be built in an antifragility manner. The antifragility is beyond the resilient system. In turn, the resilient is beyond the robust system. The robust fails when perturbations are out of its preprogramed operative range. The resilient resists shocks and stays the same. The antifragility gets better and better. To face the problem of multiscale ontological uncertainty management (Lane & Maxfield, 2005) we need application resilience and antifragility at system level first. With antifragility, system homeostatic operating equilibria can emerge out of a self-organizing landscape of self-structuring attractor points (Fiorini, 2015). Current scientific computational and simulation classic systemic tools and most sophisticated instrumentation system (developed under the positivist, reductionist paradigm and the "continuum hypothesis", CH for short) are still totally unable capture and to reliably discriminate so called "random noise" (RN) from any combinatorically optimized encoded message, called "deterministic noise" (DN) by computational information conservation theory (CICT) (Fiorini, 2014). This is the information double-bind (IDB) dilemma in current science, and nobody likes to talk about it (Fiorini, 2016). How does it come that scientists 1.0 (statisticians) are still in business without having worked out a definitive solution to the problem of the logical relationship between experience and knowledge extraction? We need to extend our systemic tools to solve this IDB dilemma first, and then to open a new era of effective, real cognitive machine intelligence (Wang et al., 2016). Proactive behavior can to some extent be modeled or simulated. If we want to support proactive behavior, prevention, for instance, we need to define a space of possibilities and to deal with variability. In fact, it is possible to conceive a convenient basic schema for Ontological Uncertainty Management (OUM) System as in Fiorini (2015). The information process describing the dynamics of reality to anticipation means to acknowledge that deterministic and non­deterministic processes are complementary. A dynamic ontological perspective can be thought of as an emergent, natural transdisciplinary reality level (TRL) (Nicolescu, 1992; 1996) from, at least, a dichotomy of two fundamental, coupled, irreducible, and complementary computational subsystems: (A) reliable unpredictability, and (B) reliable predictability subsystem respectively. From a Top-Down (TD) management perspective, the reliable predictability concept can be referred to the traditional system reactive approach (lag subsystem, closed logic, to learn and prosper) and operative management techniques. The reliable unpredictability concept can be associated with the system proactive approach (lead subsystem, open logic, to survive and grow) and strategic management techniques. In fact, to behave realistically, the system must guarantee both Logical Aperture (to survive and grow) and Logical Closure (to learn and prosper), both fed by environmental "noise" (better… from what human beings call "noise"). Anticipatory computation, inspired by anticipation processes in the living, involves learning, not only in reaction to a problem, but as a goal­action­oriented activity. The present contribution offers an innovative and original solution proposal to the problem of multiscale ontological uncertainty management for complex system by anticipation. Due to its intrinsic self-scaling properties, this system approach can be applied at any system scale: from single quantum system application development to full system governance strategic assessment policies and beyond. The reason for this modeling flexibility is the postulate that any complex system is an arbitrary multiscale system of purposive actors within continuous change. A new lesson for the children of the Anthropocene Era.