# Asking Better Questions -- The Art and Science of Forecasting: A mechanism for truer answers to high-stakes questions

arXiv: https://arxiv.org/abs/2303.18006 · HTML: https://mlsanity.com/paper/2303.18006/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)

## Built on by (most cited first, 2 of 2)

- [Large Language Model Prediction Capabilities: Evidence from a Real-World Forecasting Tournament](https://mlsanity.com/paper/2310.13014/) (2023)
- [AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy](https://mlsanity.com/paper/2402.07862/) (2024)

## Abstract (arXiv)

Without the ability to estimate and benchmark AI capability advancements, organizations are left to respond to each change reactively, impeding their ability to build viable mid and long-term strategies. This paper explores the recent growth of forecasting, a political science tool that uses explicit assumptions and quantitative estimation that leads to improved prediction accuracy. Done at the collective level, forecasting can identify and verify talent, enable leaders to build better models of AI advancements and improve inputs into design policy. Successful approaches to forecasting and case studies are examined, revealing a subclass of "superforecasters" who outperform 98% of the population and whose insights will be most reliable. Finally, techniques behind successful forecasting are outlined, including Phillip Tetlock's "Ten Commandments." To adapt to a quickly changing technology landscape, designers and policymakers should consider forecasting as a first line of defense.