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Description
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Contemporary grand strategists, political scientists, and economists have formulated various theoretical models to analyse historical events and forecast or predict future scenarios for strategic decision-makers. However, many of these models remain predominantly qualitative and narrative-led. While they provide some valuable principles and domain knowledge, they often lack empirical quantification, which limits their applications for making precise decisions over time. Traditional frameworks usually struggle to address issues such as interconnectivity, dynamics, nonlinearity, feedback, emergencies, co-evolution, unpredictability, uncertainty, and ambiguity. To address this gap, we propose a hierarchical framework underpinned by seven decision layers, which can be quantified by Theory-Informed Machine Learning (TIML) methods. This framework enables us to manage various grand strategies or strategic challenges. We argue that a grand strategy is an abstract pattern of human intelligence that emerges from multiple decision layers below and is driven by emotional rewards from above. It transcends the way of balancing means with the end and context. We consider that a grand strategy is a type of strange attractor, which is a deterministic chaos. Determinism implies that our time, resources, capability, and cultural background are bounded. Chaos entails unpredictable long-term consequences. We aim to create a new computational model that can craft a robust grand strategy driven by TIML based on chaos and complexity theories. (2026-01-01)
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