Monthly Archives: October 2010

(CONT) My answer is simply along the broader line of my work on ROBUSTNESS: goods, objects, and pieces made for pride and ego (artisans, artists, writers), not for gain (to satisfy earnings per share or academic credentials) can sustain the test of time. Which is why artisans are more ROBUST than industry.

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(CONT) My answer is simply along the broader line of my work on ROBUSTNESS: goods, objects, and pieces made for pride and ego (artisans, artists, writers), not for gain (to satisfy earnings per share or academic credentials) can sustain the test of time. Which is why artisans are more ROBUST than industry.

The non-natural & alienating aspect of technology shows in the following: an old version of a technological or modern object looks shabby today but not at the time it was new -say, an old Macbook Pro, Ipod, or an old model of your current car. Not so with more classical objects like antique furniture, fountain pens, books, stone houses, china, etc.

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The non-natural & alienating aspect of technology shows in the following: an old version of a technological or modern object looks shabby today but not at the time it was new -say, an old Macbook Pro, Ipod, or an old model of your current car. Not so with more classical objects like antique furniture, fountain pens, books, stone houses, china, etc.

SSRN-Antifragility, Robustness, and Fragility, Inside the 'Black Swan' Domain by Nassim Taleb

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Revised 10/22/10

Antifragility, Robustness, and Fragility, Inside the ‘Black Swan’ Domain




Nassim Nicholas Taleb
NYU-Poly

September 2010




Abstract:
    


This discussion makes the distinction inside the Fourth Quadrant “Black Swan Domain” between fragile and robust to model (or representational) error on the basis of convexity. The notion of model error as a convex or concave stochastic variable; why deficit forecasting errors are biased in one direction; why large is fragile to errors; how economics as a discipline made the monstrously consequential mistake of treating estimated parameters as nonstochastic variables and why this leads to fat-tails even while using Gaussian models; the notion of epistemic uncertainty as embedded in model errors.

In addition, it introduces a simple practical heuristic to measure (as an indicator of fragility) the sensitivity of a portfolio (or balance sheet) to model error. Finally, it sets an explicit path to conduct policy based on robustness.



Working Paper Series

Date posted: August 31, 2010
; Last revised: October 22, 2010

SSRN-Statistical Undecidability by Raphael Douady, Nassim Taleb

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Statistical Undecidability




Raphael Douady
Riskdata

Nassim Nicholas Taleb
NYU-Poly

October 12, 2010




Abstract:
    


Using the metadistribution of possible distributions for a given measure, we define a condition under which it is possible to make a decision based on the observation of random variable, which we call “statistical decidability”. We provide a sufficient condition on the metadistribution for the decision to be “statistically decidable” and conjecture that decisions based on a metadistribution with non compact support are always “statistically undecidable”. There is the need for a strong undefeasable a priori without which decisions are not statistically justified – an effect that is very significant for decisions affected by small probabilities.



Working Paper Series

Opacity

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Hints of the new book. HatTip to Dave Lull.

137- The Science of Antifragility, an Ignored Ubiquitous Concept

Just as a package sent by mail bears a stamp “fragile” on it, “handle with care”, imagine the opposite: “please mishandle” or “be careless”, as it benefits from shocks. Such package is said to be “antifragile”.

My next book, announced under the vulgar title Tinkering will be actually called Antifragility.

Ask anyone the antonym of fragility, they will answer robustness. Wrong -and this is a mistake made in a variety of dictionaries of synonyms and antonyms. This seems to come from a mental bias, of the same category of the mistakes we make without any self-awareness of the reasoning process involved in them; ask the same person the opposite of cold, they will answer hot, not neutral -or the opposite of destruction, they will answer construction. Clearly, therefore the opposite of unstable is not stable or sturdy; likewise the opposite of brittle is not solid -but antibrittle, or antifragile.

This seems quite a universal traits of languages (I tried Mediterranean, classical, and Semitic languages),where the notion of antifragility is totally absent.
So there are three layers:

  • fragile
  • robust: not fragile, can resist shocks.
  • antifragile: benefits from shocks, “long volatility” in trader parlance, has positive optionality. This is my life in the markets. This is my situation now: a breakdown in the banking system benefits my portfolio.

When I defined them mathematically, fragile became concave to errors; antifragile became convex (i.e., with a positive second derivative that makes it benefit from uncertainty). Actually I have written an ENTIRE book on that (Dynamic Hedging, published 14 years ago).

The absence of such word -and absence of the awareness- is symptomatic of the error of fragility: if you shoot for robustness, you ususally get robustness, but on the occasion you get fragility. If you shoot for antifragility, at worst you get robustness.

Antifragile benefits from randomness (as expressed, for a given probability distribution, by an increase in dispersion, i.e., variance for a Gaussian, etc. -just as an option benefits from an increase in volatility).

  • Evolution:it benefits from variance (up to a point). It has positive optionality.
  • Economic growth: it does not come from anything EXCEPT antifragility.
  • Creative destruction: why bailouts fragilize.