# On Single Point Forecasts for Fat-Tailed Variables

arXiv: https://arxiv.org/abs/2007.16096 · HTML: https://mlsanity.com/paper/2007.16096/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Forecasting Future World Events with Neural Networks](https://mlsanity.com/paper/2206.15474/) (2022)
- [An Imbalance-Robust Evaluation Framework for Extreme Risk Forecasts](https://mlsanity.com/paper/2512.00916/) (2025)
- [Streaming Bayesian Modeling for predicting Fat-Tailed Customer Lifetime Value](https://mlsanity.com/paper/2312.00373/) (2023)

## Abstract (arXiv)

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).