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

战略决策总是具有挑战性，因为它本质上是不确定的、模棱两可的、有风险的和复杂的。与战术和运营决策相比，战略决策是决定性的、关键的，而且往往是不可逆转的，这可能会导致长期和重大的后果。战略决策过程通常涉及探究的许多方面，包括感官知觉、审慎思考、基于探究的分析、元学习以及与外部世界的持续互动。许多未知因素、不可预测性和环境限制将影响战略决策的各个方面。传统上，这项任务通常依赖于直觉、反思性思维、远见卓识、粗略估计和实践智慧。随着最近的进展人工智能/机器学习 (AI/ML)技术，我们可以利用 AI/ML 来支持战略决策。然而，尽管取得了巨大进步，但由于模型不足，从人工智能的角度来看仍然存在很大差距。我们认为，创建决策框架的综合分类法作为表示空间对于 AI 至关重要，因为它可以提供超出当今任何人想象边界的令人惊讶的见解。战略决策是可能性的艺术。本研究开发了决策框架的系统分类法，该框架由六个基础框架、18 个分类框架和 54 个基本框架组成。我们使用基于 Bloom 分类法的查询方法来制定模型。我们的目标是奠定计算基础，以捕捉战略问题的综合景观视图。与许多传统车型相比，