Additional Key Words and PhrasesStrategic decision-making; computation; decision frames; machine learning; Additional key word and phrasesstrategic decision-making; Decision frame; Decision-making process; Decisions makings; Key words; Machine-learning; Operational decisions; Strategic decision making; Strategic decisions; Tactical decisions; Theoretical Computer Science; Computer Science (all); Strategic decision-making; General Computer Science
Abstract :
[en] 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.
Precision for document type :
Review article
Disciplines :
Computer science
Author, co-author :
WU, Caesar (ming-wei) ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > PCOG
Zhang, Rui ; Tsinghua University, Beijing, China
Kotagiri, Ramamohanarao ; Institution of Engineers Australia, Australia
BOUVRY, Pascal ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
External co-authors :
yes
Language :
English
Title :
Strategic Decisions: Survey, Taxonomy, and Future Directions from Artificial Intelligence Perspective
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