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

Authors:
Caesar Wu

The University of Melbourne, Campus Office MNO

The University of Melbourne, Campus Office MNO

0000-0002-2792-6466
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,
Rui Zhang

Tsinghua University, Beijing, China

Tsinghua University, Beijing, China

0000-0002-8132-6250
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,
Ramamohanarao Kotagiri

Institution of Engineers Australia, Broadmeadows

Institution of Engineers Australia, Broadmeadows

0000-0003-3304-9268
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,
Pascal Bouvry

University of Luxembourg, Campus Office MNO

University of Luxembourg, Campus Office MNO

0000-0001-9338-2834
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Authors Info & Claims

ACM Computing Surveys Volume 55 Issue 12Article No.: 250pp 1–30https://doi.org/10.1145/3571807

Published:02 March 2023Publication History

ACM Computing Surveys

Abstract

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.

REFERENCES

[1] Murray Williamson, Knox MacGregor, and Bernstein Alvin (Eds.). 1996. The Making of Strategy: Rulers, States, and War. Cambridge University Press. Cambridge, UK.Google Scholar
[2] Buzan Barry. 1987. An Introduction to Strategic Studies: Military Technology and International Relations. Springer. Berlin, Germany.Google ScholarCross Ref
[3] Kochenderfer Mykel J.. 2022. Algorithms for Decision Making. The MIT Press. Massachusetts, USA.Google Scholar
[4] Sànchez-Marrè Miquel. 2022. Intelligent Decision Support Systems. Springer Nature. Cham, Switzerland.Google ScholarCross Ref
[5] Marquis P., Papini O., Prade H. (Eds.). 2020. A Guided Tour of Artificial Intelligence Research: Volume I: Knowledge Representation, Reasoning and Learning. Springer Nature. Cham, Switzerland, 549–586.Google ScholarCross Ref
[6] Baltrušaitis Tadas et al. 2018. Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence (2018), 423–43. https://DOI:10.1109/TPAMI.2018.2798607Google Scholar
[7] Zhao Z. Q. et al. 2019. Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems (2019), 3212–32. https://DOI:10.1109/TNNLS.2018.2876865Google ScholarCross Ref
[8] Nilsson Nils J.. 2009. The Quest for Artificial Intelligence. Cambridge University Press. New York, USA.Google ScholarDigital Library
[9] Luger George F.. 2005. Artificial Intelligence: Structures and Strategies for Complex Problem-Solving. (5^th Ed.). Pearson Education. Essex, UK.Google Scholar
[10] O'Connell Redmond G. et al. 2018. Bridging neural and computational viewpoints on perceptual decision-making. Trends in Neurosciences (2018), 838–52.Google ScholarCross Ref
[11] Ganin Alexander A. et al. 2020. Multicriteria decision framework for cybersecurity risk assessment and management. Risk Analysis (2020), 183–99. Google ScholarCross Ref
[12] Eom Sean B. et al. 1998. A survey of decision support system applications (1988–1994). Journal of the Operational Research Society (1998), 109–20. Google ScholarCross Ref
[13] Merkert Johannes et al. 2015. A survey of the application of machine learning in decision support systems (1994–2013). ECIS. https://DOI:10.18151/7217429Google Scholar
[14] Jarrahi Mohammad H.. 2018. Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons (2018), 577–86. Google ScholarCross Ref
[15] Ackoff Russell L.. 1974. Redesigning the Future, a Systems Approach to Societal Problems. New York. 759–759. Google ScholarCross Ref
[16] Hayes Patrick J.. 1981. The logic of frames. In Readings in Artificial Intelligence. Morgan Kaufmann. 451–458. Google ScholarCross Ref
[17] Stanovich Keith E.. 2010. Decision Making and Rationality in the Modern World. Oxford University Press. New York, USA.Google Scholar
[18] Hechter Michael and Kanazawa Satoshi. 1997. Sociological rational choice theory. Annual Review of Sociology (1997), 191–214. Google ScholarCross Ref
[19] von Clausewitz Carl. 2007. On War. Oxford University Press Inc. New York, USA, 30–31, 222.Google Scholar
[20] Simon Herbert A.. 1972. Theories of bounded rationality. Decision and Organization. CBR and R. Radner. Amsterdam. North Holland (1972), 161–76.Google Scholar
[21] Damasio Antonio R.. 2005. Descartes' Error: Emotion, Reason, and the Human Brain. Random House. New York, USA.Google Scholar
[22] Rolls Edmond T.. 2014. Emotion and Decision-Making Explained. Oxford University Press. Oxford, UK.Google Scholar
[23] Minsky Marvin. 2007. The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. Simon and Schuster. New York, USA.Google Scholar
[24] Nutt Paul C.. 1998. Framing strategic decisions. Organization Science (1998), 195–216. Google ScholarDigital Library
[25] Schoemaker Paul J. H.. 2012. Profiting from Uncertainty: Strategies for Succeeding no Matter What the Future Brings. The Free Press. New York, USA.Google Scholar
[26] Domingos Pedro. 2015. The Master Algorithm: How the Quest for the Ultimate Learning Machine will Remake our World. Basic Books. New York, USA.Google Scholar
[27] Bloom Benjamin Samuel (Eds.). 1956. Taxonomy of Educational Objectives: The Classification of Educational Goals. Cognitive Domain. Longmans. Michigan, USA.Google Scholar
[28] Alex Edmonds W. and Kennedy Thomas D.. 2017. An Applied Guide to Research Designs: Quantitative, Qualitative, and Mixed Methods. Sage Publications. Thousand Oaks, CA, USA.Google ScholarCross Ref
[29] Hill Charles W. L. et al. 2014. Strategic Management: Theory & Cases: An Integrated Approach. Cengage Learning. Boston, MA, USA.Google Scholar
[30] Minsky Marvin. 1979. A framework for representing knowledge. De Gruyter (1979), 1–26. Google ScholarCross Ref
[31] Edward Russo J. and Schoemaker Paul J. H.. 2002. Winning Decisions: Getting it Right the First Time. Currency. New York, USA.Google Scholar
[32] Carter Craig R.. 2007. Behavioral supply management: A taxonomy of judgment and decision-making biases. International Journal of Physical Distribution & Logistics Management (Sep. 2007). Google ScholarCross Ref
[33] De Jaegher Kris. 2019. Strategic framing to influence clients’ risky decisions. Theory and Decision (2019), 437–462. Google ScholarCross Ref
[34] Arend Richard J.. 2020. Strategic decision-making under ambiguity: A new problem space and a proposed optimization approach. Business Research (2020), 1231–1251. Google ScholarCross Ref
[35] Haksever Cengiz et al. 2004. A model of value creation: Strategic view. Journal of Business Ethics (2004), 295–307. Google ScholarCross Ref
[36] Schoemaker Paul J. H.. 1995. Scenario planning: A tool for strategic thinking. Sloan Management Review 36, 2 (1995), 25–50. https://www.ftms.edu.my/images/Document/MOD001074%20-%20Strategic%20Management%20Analysis/WK4_SR_MOD001074_Schoemaker_1995.pdf.Google Scholar
[37] Schoemaker Paul J. H. et al. 2013. Integrating organizational networks, weak signals, strategic radars and scenario planning. Technological Forecasting and Social Change (2013), 815–824. Google ScholarCross Ref
[38] Mintzberg Henry. 1987. The strategy concept I: Five Ps for strategy. California Management Review (1987), 11–24. Google ScholarCross Ref
[39] Mintzberg Henry and Lampel Joseph. 1999. Reflecting on the strategy process. MIT Sloan Management Review 40, 3 (1999), 21. https://www.proquest.com/docview/224965534/abstract/29E36B4D6D094632PQ/1?accountid=26466.Google Scholar
[40] Bateman Thomas S. and Zeithaml Carl P.. 1988. The psychological context of strategic decisions: A model and convergent experimental findings. Strategic Management Journal (1989), 59–74. Google ScholarCross Ref
[41] Minsky Marvin. 1988. Society of Mind. Simon and Schuster, A Touchstone Book. New York, USA.Google Scholar
[42] Bouton Maxime et al. 2017. Belief state planning for autonomously navigating urban intersections. IEEE Intelligent Vehicles Symposium (IV). IEEE. (2017). Google ScholarCross Ref
[43] Siagian Christian et al. 2014. Autonomous mobile robot localization and navigation using a hierarchical map representation primarily guided by vision. Journal of Field Robotics (2014), 408–440. Google ScholarDigital Library
[44] Crowson Roy A.. 2017. Classification and Biology. Routledge. New York, USA.Google ScholarCross Ref
[45] Carper William B. and Snizek William E.. 1980. The nature and types of organisational taxonomies: An overview. Academy of Management Review (Jan. 1980), 65–75. Google ScholarCross Ref
[46] Scherpereel Christopher M.. 2006. Decision orders: A decision taxonomy. Management Decision (2006). Google ScholarCross Ref
[47] Johnson-Laird Philip N.. 1980. Mental models in cognitive science. Cognitive Science (1980), 71–115. Google ScholarCross Ref
[48] Hernes Tor and Maitlis Sally (Eds.). 2010. Process, Sensemaking, and Organizing. Oxford University Press. Oxford, UK.Google ScholarCross Ref
[49] Jonathan Baron. 2000. Thinking and Deciding. Cambridge University Press. New York, USA, 77–97.Google Scholar
[50] Horn Bernie. 2008. Framing the Future: How Progressive Values Can Win Elections and Influence People. Berrett-Koehler Publishers. San Franciso CA, USA, 66–67.Google Scholar
[51] Kahneman Daniel and Tversky Amos. 2013. Choices, Values, and Frames. In Handbook of the Fundamentals of Financial Decision Making: Part I. Cambridge University Press. New York, USA, 269–278.Google Scholar
[52] Spetzler Carl et al. 2016. Decision Quality: Value Creation From Better Business Decisions. John Wiley & Sons. New Jersey, USA.Google ScholarCross Ref
[53] Keeney Ralph L.. 1996. Value-Focused Thinking. Harvard University Press. Massachusetts, USA.Google ScholarCross Ref
[54] Solomon Robert C.. 2003. What is an Emotion?: Classic and Contemporary Readings. (2nd Ed.). Oxford University Press. New York, USA.Google Scholar
[55] Lewis Michael et al. (Eds.). 2010. Handbook of Emotions. Guilford Press. New York, USA.Google Scholar
[56] Paiva Ana. 1999. Affective Interactions: Toward a New Generation of Computer Interfaces. International Workshop on Affective Interactions. Springer. Berlin, Germany, 1–18Google Scholar
[57] Gorman Philip. 2004. Motivation and Emotion. Routledge. London, UK.Google ScholarCross Ref
[58] Myers David G. et al. 2018. Psychology. Worth Publishers. New York, USA, 459.Google Scholar
[59] Hock Roger R.. 2015. Forty Studies that Changed Psychology: Explorations into the History of Psychological Research. (7^th Ed.). Prentice-Hall, Inc. England. UK.Google Scholar
[60] Cervone Daniel and Pervin Lawrence A.. 2015. Personality: Theory and Research. (12^th Ed.). John Wiley & Sons. Danvers, MA, USA, 327–328.Google Scholar
[61] Bucholz Robert O. and Ward Joseph P.. 2012. London: A Social and Cultural History, 1550–1750. Cambridge University Press. (2012). Google ScholarCross Ref
[62] Jervis Robert. 2014. Understanding Beliefs. MIT Press. Massachusetts, USAGoogle Scholar
[63] Jervis Robert. 2006. Understanding Beliefs. John Wiley & Sons. New Jersey, USA, 641–663.Google Scholar
[64] Heuer Richards J.. 1999. Psychology of Intelligence Analysis. Center for the Study of Intelligence. Pittsburg, PA, USA.Google Scholar
[65] De Martino Benedetto et al. 2006. Frames, biases, and rational decision-making in the human brain. Science (2006), 684–687. https://DOI:10.1126/science.1128356Google Scholar
[66] Grim Patrick et al. 1998. The Philosophical Computer: Exploratory Essays in Philosophical Computer Modelling. MIT Press. Massachusetts, USA.Google ScholarCross Ref
[67] Audi Robert. 2009. The Good in the Right. Princeton University Press. New Jersey, USA.Google Scholar
[68] Bentham Jeremy. 2015. Introduction to the Principles of Morals and Legislation. Dover Publication, Inc. New York, USA.Google Scholar
[69] Simon Herbert A.. 1956. Rational choice and the structure of the environment. Psychological Review (1956), 129. Google ScholarCross Ref
[70] Selten Reinhard. 1990. Bounded rationality. Journal of Institutional and Theoretical Economics (JITE)/Zeitschrift für die gesamte Staatswissenschaft (Dec. 1990), 649–658. http://www.jstor.org/stable/40751353Google Scholar
[71] Kahneman Daniel and Tversky Amos. 2013. Prospect theory: An analysis of decision under risk. In Handbook of the Fundamentals of Financial Decision Making: Part I. World Scientific. New Jersey, USA, 99–127Google Scholar
[72] Atwater Thomas. 1992. Thinking critically about ethical issues. Teaching Philosophy (Dec. 1992), 390–393. Google ScholarCross Ref
[73] Graham Gordon. 2004. Eight Theories of Ethics. Routledge. New York, USA.Google ScholarCross Ref
[74] Schwartz Mark S.. 2016. Ethical decision-making theory: An integrated approach. Journal of Business Ethics (2016), 755–776. Google ScholarCross Ref
[75] Tenbrunsel Ann E. and Smith-Crowe Kristin. 2008. 13 ethical decision making: Where We've Been and Where We're Going. Academy of Management Annals (2008), 545–607. Google ScholarCross Ref
[76] Singer Peter (Ed.). 2013. A Companion to Ethics. John Wiley & Sons. New Jersey, USA.Google Scholar
[77] Fishburn Peter C.. 2013. The Foundations of Expected Utility. Springer Science & Business Media. Berlin, Germany.Google Scholar
[78] Pratt John Winsor et al. 1995. Introduction to Statistical Decision Theory. MIT Press. Massachusetts, USA.Google Scholar
[79] Loewenstein George F.. 1998. Frames of mind in intertemporal choice. Management Science (Feb. 1988), 200–214. Google ScholarCross Ref
[80] Read Daniel et al. 2003. Intertemporal Choice. London School of Economics and Political Science. Working Paper LSEOR 03.58. Google ScholarCross Ref
[81] Samuelson William and Zeckhauser Richard. 1988. Status quo bias in decision making. Journal of Risk and Uncertainty (1988), 7–59. Google ScholarCross Ref
[82] Johnson Eric J. and Goldstein Daniel. 2003. Do defaults save lives? Science (2003), 1338–1339. DOI: 10.1126/science.1091721Google Scholar
[83] Burka Jane and Yuen Lenora M.. 2007. Procrastination: Why you do it, what to do about it now. Da Capo Lifelong Books. Cambridge, MA, USA.Google Scholar
[84] Frederick Shane et al. 2009. Opportunity cost neglect. Journal of Consumer Research (2009), 553–561. Google ScholarCross Ref
[85] Khaldun Ibn. 2015. The Muqaddimah. An Introduction to History, Princeton Classics. New Jersey, USA.Google Scholar
[86] Tversky Amos and Kahneman Daniel. 1991. Loss aversion in riskless choice: A reference-dependent model. The Quarterly Journal of Economics (1991), 1039–1061. Google ScholarCross Ref
[87] Verplanken Bas et al. 2005. The Measurement of Habit. The Routines of Decision Making. Lawrence Erlbaum Associates, Publishers. New Jersey, USA, 231–247.Google Scholar
[88] Ouellette Judith A. and Wood Wendy. 1998. Habit and intention in everyday life: The multiple processes by which past behaviour predicts future behaviour. Psychological Bulletin (1998), 54. Google ScholarCross Ref
[89] Kahneman Daniel. 2011. Thinking, Fast and Slow. Macmillan. New York, USA.Google Scholar
[90] Sugumaran Ramanathan and Degroote John. 2011. Spatial Decision Support Systems: Principles and Practices. CRC Press. Boca Raton, FL, USA.Google Scholar
[91] Johnson Steven. 2006. The Ghost Map: The Story of London's Most Terrifying Epidemic–and How it Changed Science, Cities, and the Modern World. Penguin. New York, USA.Google Scholar
[92] Peeters Michel et al. 2013. Social stairs: Taking the piano staircase towards long-term behavioural change. International Conference on Persuasive Technology. Springer. Berlin. 174–179. Google ScholarDigital Library
[93] Miller Harvey J.. 2004. Tobler's first law and spatial analysis. Annals of the Association of American Geographers (2004), 284–289. Google ScholarCross Ref
[94] Thaler Richard H. and Sunstein Cass R.. 2008. Nudge: Improving Decisions About Health, Wealth, and Happiness, Yale University Press. London, UK.Google Scholar
[95] Naruse Makoto et al. 2018. Why is the Environment Important for Decision Making? Local Reservoir Model for Choice-Based Learning. PlOS One. (2018). Google ScholarCross Ref
[96] Yang KwangSoo and Shekhar Shashi. 2017. Spatial Network big Databases: Queries and Storage Methods. Springer. Boca Raton, FL, USA.Google ScholarCross Ref
[97] Barthélemy Marc. 2011. Spatial networks. Physics Reports (2011). Google ScholarCross Ref
[98] Watts Duncan J.. 1999. Small Worlds: The Dynamics of Networks Between Order and Randomness. Princeton: Princeton University Press. Oxfordshire, UK.Google ScholarDigital Library
[99] Oliver Dev. 2016. Spatial Network Data: Concepts and Techniques for Summarization. Springer International Publishing. Redlands CA. USA.Google ScholarCross Ref
[100] Bucur Oana et al. 2005. Representing context in an agent architecture for context-based decision making. In Proceedings of the Workshop on Context Representation and Reasoning (CRR’05), Paris, France (Jul. 2005). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.8390Google Scholar
[101] Voors Maarten et al. 2012. Exploring whether behaviour in context-free experiments is predictive of behaviour in the field: Evidence from lab and field experiments in rural Sierra Leone. Economics Letters (2012), 308–311. Google ScholarCross Ref
[102] Eugene Charniak. 1997. Statistical parsing with a context-free grammar and word statistics. AAAI/IAAI (1997), 598–603. https://dl.acm.org/doi/10.5555/1867406.1867499Google Scholar
[103] Berkeley Dina et al. (Eds.). 1998. Context-Sensitive Decision Support Systems. Springer. Boston, MA, USA, 24–40.Google Scholar
[104] Smirnov Alexander et al. 2005. Constraint-driven methodology for context-based decision support. Journal of Decision Systems (Jan. 2005), 279–301. Google ScholarCross Ref
[105] Steinberg Jonathan. 2011. Bismarck: A Life. OUP. Oxford, UK.Google Scholar
[106] Galbraith Jay R.. 2014. Designing Organizations: Strategy, Structure, and Process at the Business Unit and Enterprise Levels. John Wiley & Sons. San Francisco, CA, USA.Google Scholar
[107] Parnell Gregory S. et al. 2013. Handbook of Decision Analysis. John Wiley & Sons; San Francisco, CA, USA.Google ScholarCross Ref
[108] Tampoe Mahen. 2012. Strategic Management: Process, Content, and Implementation. Oxford University Press. Oxford, UK.Google Scholar
[109] Roijers Didederik M. and Whiteson Shimon. 2017. Multi-objective decision making. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool. Austin, Texas, USA, 1–29.Google Scholar
[110] Porter Michael E.. 2008. The five competitive forces that shape strategy. Harvard Business Review (2008), 25–40. https://hbr.org/1979/03/how-competitive-forces-shape-strategy.Google Scholar
[111] Seel Norbert M. (Ed.). 2011. Encyclopedia of the Sciences of Learning. Springer Science & Business Media. Berlin, Germany.Google Scholar
[112] Jonassen David H.. 2000. Toward a design theory of problem-solving. Educational Technology Research and Development 48, 4 (2000), 63–85. Google ScholarCross Ref
[113] Grant Edward. 1996. The Foundations of Modern Science in the Middle Ages: Their Religious, Institutional and Intellectual Contexts. Cambridge University Press. New York, USA.Google ScholarCross Ref
[114] Levinthal Daniel A.. 1997. Adaptation on rugged landscapes. Management Science (1997), 934–950. Google ScholarCross Ref
[115] Kauffman Stuart and Levin Simon. 1987. Towards a general theory of adaptive walks on rugged landscapes. Journal of Theoretical Biology (1987), 11–45. Google ScholarCross Ref
[116] McKelvey Bill. 1999. Avoiding complexity catastrophe in coevolutionary pockets: Strategies for rugged landscapes. Organization Science (Jun. 1999), 294–321. Google ScholarDigital Library
[117] Page Scott E.. 2010. Diversity and Complexity. Vol. 2. Princeton University Press. New Jersey, USA.Google ScholarDigital Library
[118] Klein Gary et al. 2008. Naturalistic decision making. Human Factors (2008), 456–460. https://doi.org/10.1518%2F001872008X288385Google Scholar
[119] Hauser Christopher K. and Salinas Emilio. 2014. Perceptual Decision Making (2014), 1–21. Google ScholarCross Ref
[120] Bruce Goldstein E. and Cacciamani Laura. 2022. Sensation and Perception. Cengage Learning. Boston, MA, USA.Google Scholar
[121] Gati Itamar et al. 1996. A taxonomy of difficulties in career decision making. Journal of Counseling Psychology (1996), 510. https://psycnet.apa.org/doi/10.1037/0022-0167.43.4.510Google ScholarCross Ref
[122] Mey Goh Yee et al. 2020. A variability taxonomy to support automation decision-making for manufacturing processes. Production Planning & Control (2020), 383–399. Google ScholarCross Ref
[123] Bunn Michele D.. 1993. Taxonomy of buying decision approaches. Journal of Marketing (1993), 38–56. Google ScholarCross Ref
[124] Gibcus Petra et al. 2009. Strategic decision making in small firms: A taxonomy of small business owners. International Journal of Entrepreneurship and Small Business (2009), 74–91. https://www.inderscienceonline.com/doi/abs/10.1504/IJESB.2009.02161.Google ScholarCross Ref
[125] Barrows Howard S.. 1986. A taxonomy of problem-based learning methods. Medical Education (1986), 481–486. Google ScholarCross Ref
[126] Hogan Mairéad et al. 2014. Theorising and testing a taxonomy of decision constructs. Journal of Customer Behaviour (2014), 171–185. Google ScholarCross Ref
[127] Johnson Melanie K. et al. 2022. Ethical decision-making models: A taxonomy of models and review of issues. Ethics & Behavior (2021), 1–6. Google ScholarCross Ref
[128] Wu Caesar et al. 2019. Value-based cloud price modelling for segmented business to business market (2019), 502–523. Google ScholarDigital Library
[129] Sabherwal Rajiv and King William R.. 1995. An empirical taxonomy of the decision-making processes concerning strategic applications of information systems. Journal of Management Information Systems (1995), 177–214. Google ScholarDigital Library
[130] Dennett Daniel. 2007. Instead of a review. Artificial Intelligence (2007), 1110–1113. https://ase.tufts.edu/cogstud/dennett/papers/reviewofHofstadter&Minsky.pdf.Google Scholar

Index Terms

Strategic Decisions: Survey, Taxonomy, and Future Directions from Artificial Intelligence Perspective
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Discourse, dialogue and pragmatics

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Published in
ACM Computing Surveys Volume 55, Issue 12
December 2023
825 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/3582891
Editor:
Albert Zomaya
University of Sydney, Australia
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Publication History
- Published: 2 March 2023
- Online AM: 24 November 2022
- Accepted: 6 November 2022
- Revised: 4 November 2022
- Received: 26 July 2021
Published in csur Volume 55, Issue 12

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Strategic decision-making
decision frames
machine learning
computation
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Strategic Decisions: Survey, Taxonomy, and Future Directions from Artificial Intelligence Perspective

ACM Computing Surveys

Abstract

REFERENCES

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