# Decision under Uncertainty

arXiv: https://arxiv.org/abs/1304.1527 · HTML: https://mlsanity.com/paper/1304.1527/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2013.

## Built on by (most cited first, 11 of 11)

- [Uncertainty measures: The big picture](https://mlsanity.com/paper/2104.06839/) (2021)
- [The intersection probability: betting with probability intervals](https://mlsanity.com/paper/2201.01729/) (2022)
- [Flexible categorization for auditing using formal concept analysis and Dempster-Shafer theory](https://mlsanity.com/paper/2210.17330/) (2022)
- [Random-Set Graph Neural Networks](https://mlsanity.com/paper/2605.11987/) (2026)
- [Fusion of evidential CNN classifiers for image classification](https://mlsanity.com/paper/2108.10233/) (2021)
- [Estimation of the qualification and behavior of a contributor and aggregation of his answers in a crowdsourcing context](https://mlsanity.com/paper/2303.04548/) (2023)
- [A geometric approach to conditioning belief functions](https://mlsanity.com/paper/2104.10651/) (2021)
- [Belief functions on ordered frames of discernment](https://mlsanity.com/paper/2210.04535/) (2022)
- [Model-free generalized fiducial inference](https://mlsanity.com/paper/2307.12472/) (2023)
- [Co-Evidential Fusion with Information Volume for Medical Image Segmentation](https://mlsanity.com/paper/2506.02492/) (2025)
- [Long-Term Sequential Decision Making under Risk](https://mlsanity.com/paper/2607.19914/) (2026)

## Abstract (arXiv)

We derive axiomatically the probability function that should be used to make decisions given any form of underlying uncertainty.