Strategic Decisions Survey, Taxonomy, and Future Directions from Artificial Intelligence Perspective
This work provides a foundational taxonomy for researchers and practitioners in AI and decision sciences, though it appears incremental as it builds on existing models by expanding coverage.
The paper tackles the challenge of strategic decision-making by developing a systematic taxonomy with 6 bases, 18 categorical, and 54 frames to capture a comprehensive view of strategic problems, covering rational and non-rational aspects across various conditions like uncertainty and complexity.
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.