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.[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][17]

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.[18] 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 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[19] 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/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 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.


The concept has been applied in physics,[5] risk analysis,[4][20] molecular biology,[7][21] transportation planning,[8][22] engineering,[11][12][23] aerospace (NASA),[13] megaproject management,[24] and computer science.[11][14][15][16][25][26]

In computer science, there is a structured proposal for an "Antifragile Software Manifesto", to react to traditional system designs.[27] The major idea is to develop antifragility by design, building a system which improves from environment's input.

See also


  1. Nassim Nicholas Taleb (2012). Antifragile: Things That Gain from Disorder. Random House. p. 430. ISBN 9781400067824.,
  2. Taleb, N.N. and Douady, R., 2013. Mathematical definition, mapping, and detection of (anti) fragility. Quantitative Finance, 13(11), pp.1677-1689.
  3. Aven, T. (2014). The Concept of Antifragility and its Implications for the Practice of Risk Analysis. Risk Analysis, 35(3), 476-483.
  4. Derbyshire, J., & Wright, G. (2014). Preparing for the future: Development of an ‘antifragile’ methodology that complements scenario planning by omitting causation. Technological Forecasting and Social Change, 82, 215-225.
  5. 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.
  7. Grube, M., Muggia, L., & Gostinčar, C. (2013). Niches and Adaptations of Polyextremotolerant Black Fungi. In Polyextremophiles (pp. 551-566). Springer Netherlands.
  8. 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. Procedia Computer Science, 32, 842-849.
  11. Jones, K. H. (2014). Engineering Antifragile Systems: A Change In Design Philosophy. Procedia Computer Science, 32, 870-875.
  12. Lichtman, M.; Vondal, M. T.; Clancy, T. C.; Reed, J. H. (2016-01-01). "Antifragile Communications" (PDF). IEEE Systems Journal. PP (99): 659–670. doi:10.1109/JSYST.2016.2517164. hdl:10919/72267. ISSN 1932-8184.
  13. Jones, Kennie H. "Antifragile Systems: An Enabler for System Engineering of Elegant Systems." (2015), NASA,
  14. 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. 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.
  16. Monperrus, Martin (2017). "Principles of Antifragile Software". Proceedings of the International Conference on the Art, Science, and Engineering of Programming - Programming '17. pp. 1–4. arXiv:1404.3056. doi:10.1145/3079368.3079412. ISBN 9781450348362.
  17. Guang, L., Nigussie, E., Plosila, J., & Tenhunen, H. (2014). Positioning Antifragility for Clouds on Public Infrastructures. Procedia Computer Science, 32, 856-861.
  18. Equihua, Miguel; Espinosa, Mariana; Gershenson, Carlos; López-Corona, Oliver; Munguia, Mariana; Pérez-Maqueo, Octavio; Ramírez-Carrillo, Elvia (2019-06-21). "Ecosystem antifragility: Beyond integrity and resilience". doi:10.7287/peerj.preprints.27813v1. Cite journal requires |journal= (help)
  19. 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. doi:10.1155/2019/3728621. ISSN 1076-2787.
  20. Aven, T. (2014). The Concept of Antifragility and its Implications for the Practice of Risk Analysis. Risk Analysis.
  21. 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.
  22. 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 requires |journal= (help)
  23. Verhulsta, E. (2014). "Applying Systems and Safety Engineering Principles for Antifragility". Procedia Computer Science, 32, 842-849.
  24. 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.
  25. Guang, L., Nigussie, E., Plosila, J., & Tenhunen, H. (2014). "Positioning Antifragility for Clouds on Public Infrastructures". Procedia Computer Science, 32, 856-861.
  26. Lichtman, Marc (2016-08-16). "Antifragile Communications". Virginia Tech. 12 (1): 659–670. Bibcode:2018ISysJ..12..659L. doi:10.1109/JSYST.2016.2517164. hdl:10919/72267.
  27. 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.
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