Antifragility

Template:Short description Antifragility is a property of systems in which they increase in capability to thrive as a result of stressors, shocks, volatility, noise, mistakes, faults, attacks, or failures. The concept was developed by 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/resilient

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 ecosystems 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. This work reformulates and builds upon the concept of resilience in a way that is mathematically conveyed and can be heuristically evaluated in real-world applications: for example, ecosystem antifragility. The authors also propose 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 have 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/cognitive

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 adaptive and antifragile is the difference between a system that is robust under volatile environments/conditions, and one that is robust in a previously unknown environment.

Mathematical heuristic

Taleb proposed a simple heuristic for detecting fragility. If $f(a)$ is some model of $a$, then fragility exists when $H<0$, robustness exists when $H=0$, and antifragility exists when $H>0$, where

$H=\frac{f(a-\Delta )+f(a+\Delta )}{2}-f(a)$.

In short, the heuristic is to adjust a model input higher and lower. If the average outcome of the model after the adjustments is significantly worse than the model baseline, then the model is fragile with respect to that input.

Applications

The concept has been applied in business and management, physics, risk analysis, molecular biology, transportation planning, urban planning, engineering, aerospace (NASA), computer science, water system design, and cancer.

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.

See also

• Complexity theory and organizations
• Hygiene hypothesis
• Information management
• Structures of organizations
  • Nodal organizational structure
• Systems theory
  • Systems engineering
  • System accident

Further reading

• Taleb, Nassim (2012). Antifragile: Things That Gain from Disorder. Random House. ISBN 978-0-8129-7968-8.
• Taleb, Nassim (2007). The Black Swan: The Impact of the Highly Improbable. Random House. ISBN 978-1-4000-6351-2.
• Taleb, Nassim (2001). Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets. Texere. ISBN 978-1-58799-071-7.
• Walker, Brian; Salt, David (2006). Resilience Thinking: Sustaining Ecosystems and People in a Changing World. Island Press. ISBN 978-1-59726-093-0.
• Cumming, Graeme (2011). Complex Adaptive Systems: An Introduction to Models of Resilience. Springer.
• McGonigal, Kelly (2015). The Upside of Stress: Why Stress Is Good for You, and How to Get Good at It. Avery. ISBN 978-1-101-98293-8.
• Bak, Per (1996). How Nature Works: The Science of Self-Organized Criticality. Copernicus.
• Dörner, Dietrich (1996). The Logic of Failure: Recognizing and Avoiding Error in Complex Situations. Basic Books. ISBN 978-0-201-47948-5.
• Taleb, Nassim (2018). Skin in the Game: Hidden Asymmetries in Daily Life. Random House. ISBN 978-0-425-28462-9.
• Leveson, Nancy (2011). Engineering a Safer World: Systems Thinking Applied to Safety. MIT Press. ISBN 978-0-262-01662-9.
• Rodin, Judith (2014). The Resilience Dividend: Being Strong in a World Where Things Go Wrong. PublicAffairs. ISBN 978-1-61039-470-3.