Antifragility

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Antifragility is a property of systems that increase in capability, resilience, or robustness 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.[1] 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,[2][3] physics,[4] molecular biology,[5][6] transportation planning,[7][8] engineering,[9][10][11] and computer science.[10][12][13][14][15]

Taleb defines it as follows in Nature:

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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 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".[16][17]

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]

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.

Applications of antifragility

The concept has been applied in physics,[4] risk analysis,[3][18] molecular biology,[6][19] transportation planning,[7][20] engineering,[10][21][22] megaproject management,[23] and computer science.[10][12][13][14][24]

In computer science, there is a structured proposal for an "Antifragile Software Manifesto", to react to traditional system designs.[25] The major idea is to develop antifragility by design, building a system which improves from environment's input.

Criticisms

Kovalenko and Sornette have argued that antifragile systems do not exist. In general, for systems subjected to variability, noise, shocks and other random perturbations, it is possible to develop strategies or designs that, on average, benefit from variability, but not any variability. Such strategies are designed to profit from the variability of particular stressors. Simultaneously, they are vulnerable to other stressors. The refusal to accept this fundamental characteristic (or intrinsic weakness) shared by any strategy or system is very dangerous, as it may lead to unexpected shocks or intended manipulations by insiders. For instance, in the financial sphere, antifragility is a name for the exploitation of a situation that turns losses for most into gains for some by special design (put option strategy) which is, however, vulnerable to non-anticipated occurrences. Moreover, the so-called antifragile strategy can contain the germs for large externalities, leading to systemic crises for which neither the strategy itself nor the system are prepared for.[26]

References

  1. ↑ 1.0 1.1 Lua error in package.lua at line 80: module 'strict' not found.
  2. ↑ Aven, T. (2014). The Concept of Antifragility and its Implications for the Practice of Risk Analysis. Risk Analysis, 35(3), 476-483.
  3. ↑ 3.0 3.1 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.
  4. ↑ 4.0 4.1 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.
  5. ↑ 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.
  6. ↑ 6.0 6.1 Grube, M., Muggia, L., & Gostinčar, C. (2013). Niches and Adaptations of Polyextremotolerant Black Fungi. In Polyextremophiles (pp. 551-566). Springer Netherlands.
  7. ↑ 7.0 7.1 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.
  8. ↑ 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).
  9. ↑ Verhulsta, E. (2014). Applying Systems and Safety Engineering Principles for Antifragility. Procedia Computer Science, 32, 842-849.
  10. ↑ 10.0 10.1 10.2 10.3 Jones, K. H. (2014). Engineering Antifragile Systems: A Change In Design Philosophy. Procedia Computer Science, 32, 870-875.
  11. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  12. ↑ 12.0 12.1 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.
  13. ↑ 13.0 13.1 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.
  14. ↑ 14.0 14.1 Monperrus, M. (2014). Principles of Antifragile Software. arXiv preprint arXiv:1404.3056.
  15. ↑ Guang, L., Nigussie, E., Plosila, J., & Tenhunen, H. (2014). Positioning Antifragility for Clouds on Public Infrastructures. Procedia Computer Science, 32, 856-861.
  16. ↑ Taleb, N. N. (2013). Philosophy:'Antifragility'as a mathematical idea. Nature, 494(7438), 430-430.
  17. ↑ [1]
  18. ↑ Aven, T. (2014). The Concept of Antifragility and its Implications for the Practice of Risk Analysis. Risk Analysis.
  19. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  20. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  21. ↑ Verhulsta, E. (2014). "Applying Systems and Safety Engineering Principles for Antifragility". Procedia Computer Science, 32, 842-849.
  22. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  23. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  24. ↑ Guang, L., Nigussie, E., Plosila, J., & Tenhunen, H. (2014). "Positioning Antifragility for Clouds on Public Infrastructures". Procedia Computer Science, 32, 856-861.
  25. ↑ Lua error in package.lua at line 80: module 'strict' not found.
  26. ↑ Tatyana Kovalenko and Didier Sornette, Dynamical Diagnosis and Solutions for Resilient Natural and Social Systems, Planet@ Risk 1 (1), 7-33 (2013) Davos, Global Risk Forum (GRF) Davos (http://arxiv.org/abs/1211.1949)
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