# On Risk-Sensitive Decision Making Under Uncertainty

arXiv: https://arxiv.org/abs/2404.13371 · HTML: https://mlsanity.com/paper/2404.13371/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2024.

## Built on by (most cited first, 1 of 1)

- [UAMDP: Uncertainty-Aware Markov Decision Process for Risk-Constrained Reinforcement Learning from Probabilistic Forecasts](https://mlsanity.com/paper/2510.08226/) (2025)

## Abstract (arXiv)

This paper studies a risk-sensitive decision-making problem under uncertainty. It considers a decision-making process that unfolds over a fixed number of stages, in which a decision-maker chooses among multiple alternatives, some of which are deterministic and others are stochastic. The decision-maker's cumulative value is updated at each stage, reflecting the outcomes of the chosen alternatives. After formulating this as a stochastic control problem, we delineate the necessary optimality conditions for it. Two illustrative examples from optimal betting and inventory management are provided to support our theory.