# Risk-Adaptive Approaches to Stochastic Optimization: A Survey

arXiv: https://arxiv.org/abs/2212.00856 · HTML: https://mlsanity.com/paper/2212.00856/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2022.

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

- [Soft ascent-descent as a stable and flexible alternative to flooding](https://mlsanity.com/paper/2310.10006/) (2023)
- [Robust Metaheuristics under Uncertainty for Berth Allocation and Quay Crane Assignment: A Review](https://mlsanity.com/paper/2608.19214/) (2026)

## Abstract (arXiv)

Uncertainty is prevalent in engineering design, data-driven problems, and decision making broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to address uncertainty by formulating and solving conservative optimization models expressed using measures of risk and related concepts. We survey the rapid development of risk measures over the last quarter century. From their beginning in financial engineering, we recount the spread to nearly all areas of engineering and applied mathematics. Solidly rooted in convex analysis, risk measures furnish a general framework for handling uncertainty with significant computational and theoretical advantages. We describe the key facts, list several concrete algorithms, and provide an extensive list of references for further reading. The survey recalls connections with utility theory and distributionally robust optimization, points to emerging applications areas such as fair machine learning, and defines measures of reliability.