# Strategic Decisions Survey, Taxonomy, and Future Directions from Artificial Intelligence Perspective

arXiv: https://arxiv.org/abs/2210.12373 · HTML: https://mlsanity.com/paper/2210.12373/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 137 references that are in the map.
- First posted on arXiv 22 Oct 2022.

## Builds on (most cited first, 3 of 3)

- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Belief State Planning for Autonomously Navigating Urban Intersections](https://mlsanity.com/paper/1704.04322/) (2017)

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

- [Trustworthy AI: Deciding What to Decide](https://mlsanity.com/paper/2311.12604/) (2023)
- [Survey of Trustworthy AI: A Meta Decision of AI](https://mlsanity.com/paper/2306.00380/) (2023)
- [Agentic AI for Self-Driving Laboratories in Soft Matter: Taxonomy, Benchmarks,and Open Challenges](https://mlsanity.com/paper/2601.17920/) (2026)

## Abstract (arXiv)

Strategic Decision-Making is always challenging because it is inherently uncertain, ambiguous, risky, and complex. It is the art of possibility. We develop a systematic taxonomy of decision-making frames that consists of 6 bases, 18 categorical, and 54 frames. We aim to lay out the computational foundation that is possible to capture a comprehensive landscape view of a strategic problem. Compared with traditional models, it covers irrational, non-rational and rational frames c dealing with certainty, uncertainty, complexity, ambiguity, chaos, and ignorance.