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

Strategic Decision-Making is always challenging because it inherently uncertain, ambiguous, risky, and complex. By contrast to tactical operational decisions, strategic decisions are decisive, pivotal, often irreversible, which may result in long-term significant consequences. A decision-making process usually involves many aspects of inquiry, including sensory perception, deliberative thinking, inquiry-based analysis, meta-learning, constant interaction with the external world. Many unknowns, unpredictabilities, environmental constraints will shape every aspect a decision. Traditionally, this task relies on intuition, reflective visionary insights, approximate estimates, practical wisdom. With recent advances artificial intelligence/machine learning (AI/ML) technologies, we can leverage AI/ML support decision-making. However, there still substantial gap from an AI perspective due inadequate models, despite tremendous progress made. We argue that creating comprehensive taxonomy decision frames as representation space essential for could offer surprising insights beyond anyone's imaginary boundary today. art possibility. This study develops systematic consists six bases, 18 categorical, 54 elementary frames. formulate model using inquiry method based Bloom's approach. aim lay out computational foundation possible capture landscape view problem. Compared traditional novel covers irrational, non-rational rational capable dealing certainty, uncertainty, complexity, ambiguity, chaos, ignorance.