 On the statistical differences between binary forecasts and real-world payoffsNassim Nicholas Taleb International Journal of Forecasting, 2020, vol. 36, issue 4, 1228-1240 Abstract: We map the difference between (univariate) binary predictions, bets and “beliefs” (expressed as a specific “event” will happen/will not happen) and real-world continuous payoffs (numerical benefits/harm from an event) and show the effect of their conflation and mischaracterization in the decision-science literature. We also examine the differences under thin and fat tails. The effects: [A] Spuriousness of many psychological results, particularly those documenting that humans overestimate tail probabilities. We quantify such conflations. [B] Being a “good forecaster” in binary space doesn’t lead to having a good actual performance, and vice versa, especially under nonlinearities. A binary forecasting record is likely to be a reverse indicator under some classes of distributions or deeper uncertainty. [C] Machine Learning: Some nonlinear payoff functions, while not lending themselves to verbalistic expressions, are well captured by ML or expressed in option contracts. Fattailedness: The difference is exacerbated in the power law classes of probability distributions. Keywords: Forecasting; Heavy tailed distributions; Extreme value theory; Forecasting competitions; Mathematical finance (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:4:p:1228-1240 DOI: 10.1016/j.ijforecast.2019.12.004 Access Statistics for this article International Journal of Forecasting is currently edited by R. J. Hyndman More articles in International Journal of Forecasting from Elsevier |
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