Antifragility is a property of systems that increase in capability to thrive as a result of stressors, shocks, volatility, noise, mistakes, faults, attacks, or failures. It is a concept developed by Professor Nassim Nicholas Taleb in his book, Antifragile, and in technical papers. As Taleb explains in his book, antifragility is fundamentally different from the concepts of resiliency (i.e. the ability to recover from failure) and robustness (that is, the ability to resist failure). The concept has been applied in risk analysis, physics, molecular biology, transportation planning, engineering, Aerospace (NASA), and computer science.
Taleb defines it as follows in a letter to Nature responding to an earlier review of his book in that journal:
Simply, antifragility is defined as a convex response to a stressor or source of harm (for some range of variation), leading to a positive sensitivity to increase in volatility (or variability, stress, dispersion of outcomes, or uncertainty, what is grouped under the designation "disorder cluster"). Likewise fragility is defined as a concave sensitivity to stressors, leading to a negative sensitivity to increase in volatility. The relation between fragility, convexity, and sensitivity to disorder is mathematical, obtained by theorem, not derived from empirical data mining or some historical narrative. It is a priori.— Taleb, N. N., Philosophy: 'Antifragility' as a mathematical idea. Nature, 2013 Feb 28; 494(7438), 430-430
Antifragile versus robust/resilientEdit
In his book, Taleb stresses the differences between antifragile and robust/resilient. "Antifragility is beyond resilience or robustness. The resilient resists shocks and stays the same; the antifragile gets better." The concept has now been applied to ecosystem in a rigorous way. In their work, the authors review the concept of ecosystem resilience in its relation to ecosystem integrity from an information theory approach. The main contribution of this work is a new way to rethink resilience, that is mathematically formal and easy to evaluate heuristically in real-world applications: ecosystem antifragility. They also porpoise that for socio-ecosystem governance, planning or in general, any decision making perspective, antifragility might be a valuable and more desirable goal to achieve than a resilience aspiration. In the same way, Pineda and co-workers has proposed a simply calculable measure of antifragility, based on the change of “satisfaction” (i.e network complexity) before and after adding perturbations, and apply it to random Boolean networks (RBNs). They also show that several well known biological networks such as Arabidopsis thaliana cell-cycle are as expected antifragile.
Antifragile versus adaptive/cognitiveEdit
An adaptive system is one that changes its behavior based on information available at time of utilization (as opposed to having the behavior defined during system design). This characteristic is sometimes referred to as cognitive. While adaptive systems allow for robustness under a variety of scenarios (often unknown during system design), they are not necessarily antifragile. In other words, the difference between antifragile and adaptive is the difference between a system that is robust under volatile environments/conditions, and one that is robust in a previously unknown environment.[clarification needed]
The concept has been applied in physics, risk analysis, molecular biology, transportation planning, engineering, aerospace (NASA), megaproject management, and computer science.
In computer science, there is a structured proposal for an "Antifragile Software Manifesto", to react to traditional system designs. The major idea is to develop antifragility by design, building a system which improves from environment's input.
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