HackerPapers

arXiv:2007.16096physics.soc-ph

On Single Point Forecasts for Fat-Tailed Variables

Nassim Nicholas Taleb, Yaneer Bar-Yam, Pasquale Cirillo

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

Abstract

We discuss common errors and fallacies when using naive "evidence based" empiricism and point forecasts for fat-tailed variables, as well as the insufficiency of using naive first-order scientific methods for tail risk management. We use the COVID-19 pandemic as the background for the discussion and as an example of a phenomenon characterized by a multiplicative nature, and what mitigating policies must result from the statistical properties and associated risks. In doing so, we also respond to the points raised by Ioannidis et al. (2020).

arXiv comments
Accepted, International Journal of Forecasting
Journal reference
DOI
Subjects
physics.soc-phPhysics and Society econ.GNGeneral Economics stat.APApplications (Statistics) stat.MEMethodology (Statistics)

On Hacker News

Linked by 1 identifiable commenter in 1 comment across 1 thread, from Jul 2022 to Jul 2022.

Comments linking this paper, per year.

Discussed in comments1 thread

  1. Voyager suspends trading, deposits, withdrawals, and loyalty rewards 1 comment

    • This is a complete misunderstanding of the problem. I invite you to read Taleb (I'm not trying to sound like a fanboy; but his ideas are literally the utterly common-sense conclusions you'll reach if you study complexity science as applied to the financial system; i.e. they are…

      concindsView on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.