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On the Difference between Binary Prediction and True Exposure With Implications For Forecasting Tournaments and Decision Making Research

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On the Difference between Binary Prediction and True Exposure With Implications For Forecasting Tournaments and Decision Making Research

Nassim N. Taleb, Philip E. Tetlock

Abstract
There are serious statistical differences between predictions, bets, and exposures that have a yes/no type of payoff, the “binaries”, and those that have varying payoffs, which we call the “variable”. Real world exposures tend to belong to the variable category, and are poorly captured by binaries. Yet much of the economics and decision making literature confuses the two. variable exposures are sensitive to Black Swan effects, model errors, and prediction problems, while the binaries are largely immune to them. The binaries are mathematically tractable, while the variable are much less so. Hedging variable exposures with binary bets can be disastrous–and because of the human tendency to engage in attribute substitution when confronted by difficult questions,decision-makers and researchers often confuse the variable for the binary.

https://docs.google.com/file/d/0B_31K_MP92hUZFRrQk1VOExqMUE/edit?pli=1

I added a section on GMOs in the “Small is Beautiful” paper…

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I added a section on GMOs in the “Small is Beautiful” paper. Remarkably any concentration (loss of diversity) acts the same way in the fragilization of systems.

Progressively zooming-in on GMOs… There is a bigger mathematical argument linked to “ecocide” not in this piece. Fail to understand how someone can claim “science” in their support or positions against them “unscientific” or “irrational”.

smallisbeautiful.pdf – Google Drive

via (1) I added a section on GMOs in the “Small is… – Nassim Nicholas Taleb.

Mathematical Definition, Mapping, and Detection of (Anti)Fragility by Nassim Nicholas Taleb, Raphael Douady :: SSRN

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We provide a mathematical definition of fragility and antifragility as negative or positive sensitivity to a semi-measure of dispersion and volatility (a variant of negative or positive “vega”) and examine the link to nonlinear effects. We integrate model error (and biases) into the fragile or antifragile context. Unlike risk, which is linked to psychological notions such as subjective preferences (hence cannot apply to a coffee cup) we offer a measure that is universal and concerns any object that has a probability distribution (whether such distribution is known or, critically, unknown).

We propose a detection of fragility, robustness, and antifragility using a single “fast-and-frugal”, model-free, probability free heuristic that also picks up exposure to model error. The heuristic lends itself to immediate implementation, and uncovers hidden risks related to company size, forecasting problems, and bank tail exposures (it explains the forecasting biases). While simple to implement, it outperforms stress testing and other such methods such as Value-at-Risk.

via Mathematical Definition, Mapping, and Detection of (Anti)Fragility by Nassim Nicholas Taleb, Raphael Douady :: SSRN.

Friends, some advice. It looks like a few academic presses…

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Friends, some advice. It looks like a few academic presses are interested in publishing my mathematical book (with some peer-reviewing). It would be the technical version of the INCERTO and I will keep the copyright to include as appendices in the consolidated volume.
Which title works best?

a- Lectures on Probability
b- Probability and Risk in the Real World
c- Treatise on Risk and Probability
d- Treatise on Risk
e- Probability, Risk, and (Anti)Fragility
f- Probability, Fat Tails, and Fragility
e- ?

I will keep a free copy here or TBA
https://docs.google.com/file/d/0B_31K_MP92hUVjNBUFB5VDZOMDg/edit?usp=sharing

Friends, some advice. It looks like a few… – Nassim Nicholas Taleb.

On the Difference between Binary Prediction and True Exposure with Implications for Forecasting Tournaments and Decision Making Research | SSRN

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Revision

On the Difference between Binary Prediction and True Exposure with Implications for Forecasting Tournaments and Decision Making Research

Nassim Nicholas Taleb

NYU-Poly; Université Paris I Panthéon-Sorbonne – Centre d’Economie de la Sorbonne (CES)

Philip E. Tetlock

University of California, Berkeley – Organizational Behavior & Industrial Relations Group; University of Pennsylvania – Management Department

June 25, 2013

Abstract:

There are serious differences between predictions, bets, and exposures that have a yes/no type of payoff, the “binaries”, and those that have varying payoffs, which we call the “vanilla”. Real world exposures tend to belong to the vanilla category, and are poorly captured by binaries. Vanilla exposures are sensitive to Black Swan effects, model errors, and prediction problems, while the binaries are largely immune to them. The binaries are mathematically tractable, while the vanilla are much less so. Hedging vanilla exposures with binary bets can be disastrous — and because of the human tendency to engage in attribute substitution when confronted by difficult questions, decision-makers and researchers often confuse the vanilla for the binary.

Number of Pages in PDF File: 7

Keywords: Predictions, Risk, Decision, Judgment and Decision Making, Fat Tails

working papers series

via On the Difference between Binary Prediction and True Exposure with Implications for Forecasting Tournaments and Decision Making Research by Nassim Nicholas Taleb, Philip E. Tetlock :: SSRN.