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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.

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

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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.