Antifragility

Last updated

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. [1] [2] 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, [3] [4] physics, [5] molecular biology, [6] [7] transportation planning, [8] [9] engineering, [10] [11] [12] aerospace (NASA), [13] and computer science. [11] [14] [15] [16]

Contents

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." [1] The concept has now been applied to ecosystems in a rigorous way. [17] 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 [18] 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.[ clarification needed ]

Mathematical heuristic

Taleb proposed a simple heuristic [19] for detecting fragility. If is some model of , then fragility exists when , robustness exists when , and antifragility exists when , where

.

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, [20] physics, [5] risk analysis, [4] [21] molecular biology, [7] [22] transportation planning, [8] [23] urban planning, [24] [25] [26] engineering, [11] [12] [27] aerospace (NASA), [13] megaproject management, [28] computer science, [11] [14] [15] [29] [30] and water system design. [31]

In computer science, there is a structured proposal for an "Antifragile Software Manifesto", to react to traditional system designs. [32] The major idea is to develop antifragility by design, building a system which improves from environment's input.

See also

Related Research Articles

A* is a graph traversal and pathfinding algorithm, which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. Given a weighted graph, a source node and a goal node, the algorithm finds the shortest path from source to goal.

<span class="mw-page-title-main">Nassim Nicholas Taleb</span> Lebanese-American author (born 1960)

Nassim Nicholas Taleb is a Lebanese-American essayist, mathematical statistician, former option trader, risk analyst, and aphorist. His work concerns problems of randomness, probability, and uncertainty.

Robust optimization is a field of mathematical optimization theory that deals with optimization problems in which a certain measure of robustness is sought against uncertainty that can be represented as deterministic variability in the value of the parameters of the problem itself and/or its solution. It is related to, but often distinguished from, probabilistic optimization methods such as chance-constrained optimization.

Vulnerability management is the "cyclical practice of identifying, classifying, prioritizing, remediating, and mitigating" software vulnerabilities. Vulnerability management is integral to computer security and network security, and must not be confused with vulnerability assessment.

The scenario approach or scenario optimization approach is a technique for obtaining solutions to robust optimization and chance-constrained optimization problems based on a sample of the constraints. It also relates to inductive reasoning in modeling and decision-making. The technique has existed for decades as a heuristic approach and has more recently been given a systematic theoretical foundation.

A social-ecological system consists of 'a bio-geo-physical' unit and its associated social actors and institutions. Social-ecological systems are complex and adaptive and delimited by spatial or functional boundaries surrounding particular ecosystems and their context problems.

<i>Antifragile</i> (book) 2012 book by Nassim Nicholas Taleb

Antifragile: Things That Gain From Disorder is a book by Nassim Nicholas Taleb published on November 27, 2012, by Random House in the United States and Penguin in the United Kingdom. This book builds upon ideas from his previous works including Fooled by Randomness (2001), The Black Swan (2007–2010), and The Bed of Procrustes (2010–2016), and is the fourth book in the five-volume philosophical treatise on uncertainty titled Incerto. Some of the ideas are expanded on in Taleb's fifth book Skin in the Game: Hidden Asymmetries in Daily Life (2018).

Dana Klisanin is a psychologist, futurist, and author best known for her research and writing in the field of digital altruism and the impact of the digital era on heroism. Her research was recognized in 2012 by the American Psychological Association with an award for Early Career Scientific Contribution to Media Psychology. In 2017, Klisanin was the recipient of the President's Outstanding Woman Futurist Award, as recognized by the World Futures Studies Federation (WFSF). In 2020, Klisanin was named one Forbes' "50 Leading Female Futurists." As a novelist, Klisanin is the author of Future Hack, the first installment in a series entitled Chronicles of G.A.I.A.

<span class="mw-page-title-main">Resilience (engineering and construction)</span> Infrastructure design able to absorb damage without suffering complete failure

In the fields of engineering and construction, resilience is the ability to absorb or avoid damage without suffering complete failure and is an objective of design, maintenance and restoration for buildings and infrastructure, as well as communities. A more comprehensive definition is that it is the ability to respond, absorb, and adapt to, as well as recover in a disruptive event. A resilient structure/system/community is expected to be able to resist to an extreme event with minimal damages and functionality disruptions during the event; after the event, it should be able to rapidly recovery its functionality similar to or even better than the pre-event level.

<span class="mw-page-title-main">Raphael Douady</span> French mathematician and economist

Raphael Douady is a French mathematician and economist. He holds the Robert Frey Endowed Chair for Quantitative Finance at Stony Brook, New York. He is a fellow of the Centre d’Economie de la Sorbonne, Paris 1 Pantheon-Sorbonne University, and academic director of the Laboratory of Excellence on Financial Regulation.

The Lindy effect is a theorized phenomenon by which the future life expectancy of some non-perishable things, like a technology or an idea, is proportional to their current age. Thus, the Lindy effect proposes the longer a period something has survived to exist or be used in the present, the longer its remaining life expectancy. Longevity implies a resistance to change, obsolescence, or competition, and greater odds of continued existence into the future. Where the Lindy effect applies, mortality rate decreases with time. Mathematically, the Lindy effect corresponds to lifetimes following a Pareto probability distribution.

Robustness, the ability to withstand failures and perturbations, is a critical attribute of many complex systems including complex networks.

Community resilience is the sustained ability of a community to use available resources to respond to, withstand, and recover from adverse situations. This allows for the adaptation and growth of a community after disaster strikes. Communities that are resilient are able to minimize any disaster, making the return to normal life as effortless as possible. By implementing a community resilience plan, a community can come together and overcome any disaster, while rebuilding physically and economically.

<span class="mw-page-title-main">Nature-based solutions</span> Sustainable management and use of nature for tackling socio-environmental challenges

Nature-based solutions is the sustainable management and use of natural processes to tackle socio-environmental issues. These issues include for example climate change mitigation and adaptation, water security, and disaster risk reduction. The aim is that resilient ecosystems provide solutions for the benefit of both societies and biodiversity. The 2019 UN Climate Action Summit highlighted nature-based solutions as an effective method to combat climate change. For example, nature-based systems for climate change adaptation can include natural flood management, restoring natural coastal defences, and providing local cooling.

<span class="mw-page-title-main">Munther A. Dahleh</span>

Munther A. Dahleh is the William Coolidge Professor of electrical engineering and computer science and director of the Massachusetts Institute of Technology (MIT) Institute for Data, Systems, and Society (IDSS).

In regression analysis, an interval predictor model (IPM) is an approach to regression where bounds on the function to be approximated are obtained. This differs from other techniques in machine learning, where usually one wishes to estimate point values or an entire probability distribution. Interval Predictor Models are sometimes referred to as a nonparametric regression technique, because a potentially infinite set of functions are contained by the IPM, and no specific distribution is implied for the regressed variables.

This is a chronological table of metaheuristic algorithms that only contains fundamental algorithms. Hybrid algorithms and multi-objective algorithms are not listed in the table below.

Internet of vehicles (IoV) is a network of vehicles equipped with sensors, software, and the technologies that mediate between these with the aim of connecting & exchanging data over the Internet according to agreed standards. IoV evolved from Vehicular Ad Hoc Networks, and is expected to ultimately evolve into an "Internet of autonomous vehicles". It is expected that IoV will be one of the enablers for an autonomous, connected, electric, and shared (ACES) Future Mobility.

Ordinal priority approach (OPA) is a multiple-criteria decision analysis method that aids in solving the group decision-making problems based on preference relations.

<span class="mw-page-title-main">Resilience (mathematics)</span> Mathematical measure of transient behavior.

In mathematical modeling, resilience refers to the ability of a dynamical system to recover from perturbations and return to its original stable steady state. It is a measure of the stability and robustness of a system in the face of changes or disturbances. If a system is not resilient enough, it is more susceptible to perturbations and can more easily undergo a critical transition. A common analogy used to explain the concept of resilience of an equilibrium is one of a ball in a valley. A resilient steady state corresponds to a ball in a deep valley, so any push or perturbation will very quickly lead the ball to return to the resting point where it started. On the other hand, a less resilient steady state corresponds to a ball in a shallow valley, so the ball will take a much longer time to return to the equilibrium after a perturbation.

References

  1. 1 2 Nassim Nicholas Taleb (2012). Antifragile: Things That Gain from Disorder . Random House. p.  430. ISBN   9781400067824. antifragile Mistaking the source of important or even necessary.,
  2. ↑ Taleb, N.N.; Douady, R. (2013). "Mathematical definition, mapping, and detection of (anti) fragility". Quantitative Finance. 13 (11): 1677–1689. arXiv: 1208.1189 . doi:10.1080/14697688.2013.800219. S2CID   219716527.
  3. ↑ Aven, T (2014). "The Concept of Antifragility and its Implications for the Practice of Risk Analysis". Risk Analysis. 35 (3): 476–483. doi:10.1111/risa.12279. PMID   25263809. S2CID   5537979.
  4. 1 2 Derbyshire, J.; Wright, G. (2014). "Preparing for the future: Development of an 'antifragile' methodology that complements scenario planning by omitting causation" (PDF). Technological Forecasting and Social Change. 82: 215–225. doi:10.1016/j.techfore.2013.07.001.
  5. 1 2 Naji, A., Ghodrat, M., Komaie-Moghaddam, H., & Podgornik, R. (2014). Asymmetric Coulomb fluids at randomly charged dielectric interfaces: Anti-fragility, overcharging and charge inversion. J. Chem. Phys. 141 174704.
  6. ↑ Danchin, A.; Binder, P. M.; Noria, S. (2011). "Antifragility and tinkering in biology (and in business) flexibility provides an efficient epigenetic way to manage risk". Genes. 2 (4): 998–1016. doi: 10.3390/genes2040998 . PMC   3927596 . PMID   24710302.
  7. 1 2 Grube, Martin; Muggia, Lucia; Gostinčar, Cene (2013). "Niches and Adaptations of Polyextremotolerant Black Fungi". Polyextremophiles. Cellular Origin, Life in Extreme Habitats and Astrobiology. Vol. 27. pp. 551–566. doi:10.1007/978-94-007-6488-0_25. ISBN   978-94-007-6487-3.
  8. 1 2 Levin, J. S., Brodfuehrer, S. P., & Kroshl, W. M. (2014, March). Detecting antifragile decisions and models lessons from a conceptual analysis model of Service Life Extension of aging vehicles. In Systems Conference (SysCon), 2014 8th Annual IEEE (pp. 285-292). IEEE.
  9. ↑ Isted, R. (2014, August). The use of antifragility heuristics in transport planning. In Australian Institute of Traffic Planning and Management (AITPM) National Conference, 2014, Adelaide, South Australia, Australia (No. 3).
  10. ↑ Verhulsta, E (2014). "Applying Systems and Safety Engineering Principles for Antifragility" (PDF). Procedia Computer Science. 32: 842–849. doi: 10.1016/j.procs.2014.05.500 .
  11. 1 2 3 4 Jones, K. H. (2014). "Engineering Antifragile Systems: A Change In Design Philosophy". Procedia Computer Science. 32: 870–875. doi: 10.1016/j.procs.2014.05.504 . hdl: 2060/20140010075 .
  12. 1 2 Lichtman, M.; Vondal, M. T.; Clancy, T. C.; Reed, J. H. (2016-01-01). "Antifragile Communications". IEEE Systems Journal. PP (99): 659–670. Bibcode:2018ISysJ..12..659L. doi:10.1109/JSYST.2016.2517164. hdl: 10919/72267 . ISSN   1932-8184. S2CID   4339184.
  13. 1 2 Jones, Kennie H. "Antifragile Systems: An Enabler for System Engineering of Elegant Systems." (2015), NASA,
  14. 1 2 Ramirez, C. A., & Itoh, M. (2014, September). An initial approach towards the implementation of human error identification services for antifragile systems. In SICE Annual Conference (SICE), 2014 Proceedings of the (pp. 2031-2036). IEEE.
  15. 1 2 Abid, A.; Khemakhem, M. T.; Marzouk, S.; Jemaa, M. B.; Monteil, T.; Drira, K. (2014). "Toward Antifragile Cloud Computing Infrastructures". Procedia Computer Science. 32: 850–855. doi: 10.1016/j.procs.2014.05.501 .
  16. ↑ Guang, L.; Nigussie, E.; Plosila, J.; Tenhunen, H. (2014). "Positioning Antifragility for Clouds on Public Infrastructures". Procedia Computer Science. 32: 856–861. doi: 10.1016/j.procs.2014.05.502 .
  17. ↑ Equihua, Miguel; Espinosa, Mariana; Gershenson, Carlos; López-Corona, Oliver; Munguia, Mariana; Pérez-Maqueo, Octavio; Ramírez-Carrillo, Elvia (2020). "Ecosystem antifragility: Beyond integrity and resilience". PeerJ. 8: e8533. doi: 10.7717/peerj.8533 . PMC   7020813 . PMID   32095358.
  18. ↑ Pineda, Omar K.; Kim, Hyobin; Gershenson, Carlos (2019-05-28). "A Novel Antifragility Measure Based on Satisfaction and Its Application to Random and Biological Boolean Networks". Complexity. 2019: 1–10. arXiv: 1812.06760 . doi: 10.1155/2019/3728621 . ISSN   1076-2787.
  19. ↑ Taleb, Nassim Nicholas; Canetti, Elie; Kinda, Tidiane; Loukoianova, Elena; Schmieder, Christian (2012-08-01). "A New Heuristic Measure of Fragility and Tail Risks: Application to Stress Testing". Rochester, NY. SSRN   2156095.{{cite journal}}: Cite journal requires |journal= (help)
  20. ↑ Nikookar, Ethan; Varsei, Mohsen; Wieland, Andreas (2021). "Gaining from disorder: Making the case for antifragility in purchasing and supply chain management". Journal of Purchasing and Supply Management. 27 (3): 100699. doi: 10.1016/j.pursup.2021.100699 .
  21. ↑ Aven, Terje (2015). "The Concept of Antifragility and its Implications for the Practice of Risk Analysis". Risk Analysis. 35 (3): 476–483. doi:10.1111/risa.12279. PMID   25263809. S2CID   5537979.
  22. ↑ Antoine Danchin; Philippe M. Binder; Stanislas Noria (2011). "Antifragility and Tinkering in Biology (and in Business) Flexibility Provides an Efficient Epigenetic Way to Manage Risk". Genes. 2 (4): 998–1016. doi: 10.3390/genes2040998 . PMC   3927596 . PMID   24710302.
  23. ↑ Isted, Richard (August 2014). "The Use of Anti-Fragility Heuristics in Transport Planning" (3). Adelaide, South Australia: Australian Institute of Traffic Planning and Management National Conference. Archived from the original on 2016-03-03. Retrieved 2016-02-01.{{cite journal}}: Cite journal requires |journal= (help)
  24. ↑ Blečić, Ivan; Cecchini, Arnaldo (2019-09-12). "Antifragile planning". Planning Theory. 19 (2): 172–192. doi:10.1177/1473095219873365. hdl: 11584/278497 . ISSN   1473-0952. S2CID   219975474.
  25. ↑ Roggema, Rob (2019-02-21). "Design for Disruption: Creating Anti-Fragile Urban Delta Landscapes". Urban Planning. 4 (1): 113–122. doi: 10.17645/up.v4i1.1469 . ISSN   2183-7635.
  26. ↑ Redmond, Alan Martin; Vlachopanagiotis, Theocharis; Moschopoulou, Aikaterini; Grizos, Konstandinos; Manthos, Evangelos; Rezgui, Yacine (2023). "Antifragile Cities – Decision Support Tools to Support the Implementation of the Climate-neutral and Smart Cities" (PDF). MODERN SYSTEMS 2023 : International Conference of Modern Systems Engineering Solutions - 2023.
  27. ↑ Verhulsta, E (2014). "Applying Systems and Safety Engineering Principles for Antifragility" (PDF). Procedia Computer Science. 32: 842–849. doi: 10.1016/j.procs.2014.05.500 .
  28. ↑ Atif Ansar; Bent Flyvbjerg; Alexander Budzier; Daniel Lunn (2016). "Big is Fragile: An Attempt at Theorizing Scale". The Oxford Handbook of Megaproject Management, Oxford University Press. arXiv: 1603.01416 . Bibcode:2016arXiv160301416A. SSRN   2741198.
  29. ↑ Guang, L.; Nigussie, E.; Plosila, J.; Tenhunen, H. (2014). "Positioning Antifragility for Clouds on Public Infrastructures". Procedia Computer Science. 32: 856–861. doi: 10.1016/j.procs.2014.05.502 .
  30. ↑ Lichtman, Marc (2018). "Antifragile Communications". IEEE Systems Journal. 12 (1): 659–670. Bibcode:2018ISysJ..12..659L. doi:10.1109/JSYST.2016.2517164. hdl: 10919/72267 . S2CID   4339184 . Retrieved September 27, 2020.
  31. ↑ Goodwill, Joseph E.; Ray, Patrick; Nock, Destenie; Miller, Christopher M. (2021-12-23). "Emerging investigator series: moving beyond resilience by considering antifragility in potable water systems". Environmental Science: Water Research & Technology. 8 (1): 8–21. doi:10.1039/D1EW00732G. ISSN   2053-1419. S2CID   244063552.
  32. ↑ Russo, Daniel; Ciancarini, Paolo (2016-01-01). "A Proposal for an Antifragile Software Manifesto". Procedia Computer Science. The 7th International Conference on Ambient Systems, Networks and Technologies (ANT 2016) / The 6th International Conference on Sustainable Energy Information Technology (SEIT-2016) / Affiliated Workshops. 83: 982–987. doi: 10.1016/j.procs.2016.04.196 .
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.