Category Archives: Papers

The Problem is Beyond Psychology: The Real World is More Random than Regression Analyses by Nassim Taleb, Daniel Goldstein :: SSRN

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The Problem is Beyond Psychology: The Real World is More Random than Regression Analyses


Nassim Nicholas Taleb

NYU-Poly

Daniel G. Goldstein

London Business School

International Journal of Forecasting, Forthcoming

Abstract:
   
Where the problem is not expert underestimation of randomness, but more: the tools themselves used in regression analyses and similar methods underestimate fat tails, hence the randomness in the data. We should avoid imparting psychological explanations to errors in the use of statistical methods.

[Updated: The Problem is Beyond Psychology 2012]

Nassim Taleb Papers – Recent Revisions

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NNT’s Author Page at SSRN.

New: A Map and Simple Heuristic to Detect Fragility, Antifragility, and Model Error Date posted: June 15, 2011

The Future Has Thicker Tails than the Past: Model Error as Branching Counterfactuals Date posted: May 24, 2011

Finiteness of Variance is Irrelevant in the Practice of Quantitative Finance Date posted: June 09, 2008 ; Last revised: June 08, 2011

The Illusions of Dynamic Replication Date posted: May 09, 2005 ; Last revised: June 08, 2011

Errors, Robustness, and the Fourth Quadrant Date posted: February 14, 2009 ; Last revised: June 08, 2011

Antifragility, Robustness, and Fragility, Inside the ‘Black Swan’ Domain Date posted: August 31, 2010 ; Last revised: June 06, 2011


 

The Future Has Thicker Tails than the Past: Model Error as Branching Counterfactuals by Nassim Taleb :: SSRN

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The Future Has Thicker Tails than the Past: Model Error as Branching Counterfactuals




Nassim Nicholas Taleb


NYU-Poly

May 23, 2011




Abstract:
    


Ex ante predicted outcomes should be interpreted as counterfactuals (potential histories), with errors as the spread between outcomes. But error rates have error rates. We reapply measurements of uncertainty about the estimation errors of the estimation errors of an estimation treated as branching counterfactuals. Such recursions of epistemic uncertainty have markedly different distributial properties from conventional sampling error, and lead to fatter tails in the projections than in past realizations. Counterfactuals of error rates always lead to fat tails, regardless of the probability distribution used. A mere .01% branching error rate about the STD (itself an error rate), and .01% branching error rate about that error rate, etc. (recursing all the way) results in explosive (and infinite) moments higher than 1. Missing any degree of regress leads to the underestimation of small probabilities and concave payoffs (a standard example of which is Fukushima). The paper states the conditions under which higher order rates of uncertainty (expressed in spreads of counterfactuals) alters the shapes the of final distribution and shows which a priori beliefs about conterfactuals are needed to accept the reliability of conventional probabilistic methods (thin tails or mildly fat tails).

Number of Pages in PDF File: 11

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

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Shared by JohnH

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