survey

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

Authors:

Ramamohanarao Kotagiri,

Pascal BouvryAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 12

Article No.: 250, Pages 1 - 30

https://doi.org/10.1145/3571807

Published: 02 March 2023 Publication History

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]

Williamson Murray, MacGregor Knox, and Alvin Bernstein (Eds.). 1996. The Making of Strategy: Rulers, States, and War. Cambridge University Press. Cambridge, UK.

[2]

Barry Buzan. 1987. An Introduction to Strategic Studies: Military Technology and International Relations. Springer. Berlin, Germany.

[3]

Mykel J. Kochenderfer. 2022. Algorithms for Decision Making. The MIT Press. Massachusetts, USA.

[4]

Miquel Sànchez-Marrè. 2022. Intelligent Decision Support Systems. Springer Nature. Cham, Switzerland.

[5]

P. Marquis, O. Papini, H. Prade (Eds.). 2020. A Guided Tour of Artificial Intelligence Research: Volume I: Knowledge Representation, Reasoning and Learning. Springer Nature. Cham, Switzerland, 549–586.

[6]

Tadas Baltrušaitis 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.2018.2798607

[7]

Z. Q. Zhao 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.2018.2876865

[8]

Nils J. Nilsson. 2009. The Quest for Artificial Intelligence. Cambridge University Press. New York, USA.

Digital Library

[9]

George F. Luger. 2005. Artificial Intelligence: Structures and Strategies for Complex Problem-Solving. (5^th Ed.). Pearson Education. Essex, UK.

[10]

G. O'Connell Redmond et al. 2018. Bridging neural and computational viewpoints on perceptual decision-making. Trends in Neurosciences (2018), 838–52.

[11]

Alexander A. Ganin et al. 2020. Multicriteria decision framework for cybersecurity risk assessment and management. Risk Analysis (2020), 183–99.

[12]

Sean B. Eom et al. 1998. A survey of decision support system applications (1988–1994). Journal of the Operational Research Society (1998), 109–20.

[13]

Johannes Merkert et al. 2015. A survey of the application of machine learning in decision support systems (1994–2013). ECIS. https://DOI:10.18151/7217429

[14]

Mohammad H. Jarrahi. 2018. Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons (2018), 577–86.

[15]

Russell L. Ackoff. 1974. Redesigning the Future, a Systems Approach to Societal Problems. New York. 759–759.

[16]

Patrick J. Hayes. 1981. The logic of frames. In Readings in Artificial Intelligence. Morgan Kaufmann. 451–458.

[17]

Keith E. Stanovich. 2010. Decision Making and Rationality in the Modern World. Oxford University Press. New York, USA.

[18]

Michael Hechter and Satoshi Kanazawa. 1997. Sociological rational choice theory. Annual Review of Sociology (1997), 191–214.

[19]

Carl von Clausewitz. 2007. On War. Oxford University Press Inc. New York, USA, 30–31, 222.

[20]

Herbert A. Simon. 1972. Theories of bounded rationality. Decision and Organization. CBR and R. Radner. Amsterdam. North Holland (1972), 161–76.

[21]

Antonio R. Damasio. 2005. Descartes' Error: Emotion, Reason, and the Human Brain. Random House. New York, USA.

[22]

Edmond T. Rolls. 2014. Emotion and Decision-Making Explained. Oxford University Press. Oxford, UK.

[23]

Marvin Minsky. 2007. The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. Simon and Schuster. New York, USA.

[24]

Paul C. Nutt. 1998. Framing strategic decisions. Organization Science (1998), 195–216.

Digital Library

[25]

Paul J. H. Schoemaker. 2012. Profiting from Uncertainty: Strategies for Succeeding no Matter What the Future Brings. The Free Press. New York, USA.

[26]

Pedro Domingos. 2015. The Master Algorithm: How the Quest for the Ultimate Learning Machine will Remake our World. Basic Books. New York, USA.

[27]

Benjamin Samuel Bloom (Eds.). 1956. Taxonomy of Educational Objectives: The Classification of Educational Goals. Cognitive Domain. Longmans. Michigan, USA.

[28]

W. Alex Edmonds and Thomas D. Kennedy. 2017. An Applied Guide to Research Designs: Quantitative, Qualitative, and Mixed Methods. Sage Publications. Thousand Oaks, CA, USA.

[29]

Charles W. L. Hill et al. 2014. Strategic Management: Theory & Cases: An Integrated Approach. Cengage Learning. Boston, MA, USA.

[30]

Marvin Minsky. 1979. A framework for representing knowledge. De Gruyter (1979), 1–26.

[31]

J. Edward Russo and Paul J. H. Schoemaker. 2002. Winning Decisions: Getting it Right the First Time. Currency. New York, USA.

[32]

Craig R. Carter. 2007. Behavioral supply management: A taxonomy of judgment and decision-making biases. International Journal of Physical Distribution & Logistics Management (Sep. 2007).

[33]

Kris De Jaegher. 2019. Strategic framing to influence clients’ risky decisions. Theory and Decision (2019), 437–462.

[34]

Richard J. Arend. 2020. Strategic decision-making under ambiguity: A new problem space and a proposed optimization approach. Business Research (2020), 1231–1251.

[35]

Cengiz Haksever et al. 2004. A model of value creation: Strategic view. Journal of Business Ethics (2004), 295–307.

[36]

Paul J. H. Schoemaker. 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.

[37]

Paul J. H. Schoemaker et al. 2013. Integrating organizational networks, weak signals, strategic radars and scenario planning. Technological Forecasting and Social Change (2013), 815–824.

[38]

Henry Mintzberg. 1987. The strategy concept I: Five Ps for strategy. California Management Review (1987), 11–24.

[39]

Henry Mintzberg and Joseph Lampel. 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.

[40]

Thomas S. Bateman and Carl P. Zeithaml. 1988. The psychological context of strategic decisions: A model and convergent experimental findings. Strategic Management Journal (1989), 59–74.

[41]

Marvin Minsky. 1988. Society of Mind. Simon and Schuster, A Touchstone Book. New York, USA.

[42]

Maxime Bouton et al. 2017. Belief state planning for autonomously navigating urban intersections. IEEE Intelligent Vehicles Symposium (IV). IEEE. (2017).

[43]

Christian Siagian 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.

Digital Library

[44]

Roy A. Crowson. 2017. Classification and Biology. Routledge. New York, USA.

[45]

William B. Carper and William E. Snizek. 1980. The nature and types of organisational taxonomies: An overview. Academy of Management Review (Jan. 1980), 65–75.

[46]

Christopher M. Scherpereel. 2006. Decision orders: A decision taxonomy. Management Decision (2006).

[47]

Philip N. Johnson-Laird. 1980. Mental models in cognitive science. Cognitive Science (1980), 71–115.

[48]

Tor Hernes and Sally Maitlis (Eds.). 2010. Process, Sensemaking, and Organizing. Oxford University Press. Oxford, UK.

[49]

Baron Jonathan. 2000. Thinking and Deciding. Cambridge University Press. New York, USA, 77–97.

[50]

Bernie Horn. 2008. Framing the Future: How Progressive Values Can Win Elections and Influence People. Berrett-Koehler Publishers. San Franciso CA, USA, 66–67.

[51]

Daniel Kahneman and Amos Tversky. 2013. Choices, Values, and Frames. In Handbook of the Fundamentals of Financial Decision Making: Part I. Cambridge University Press. New York, USA, 269–278.

[52]

Carl Spetzler et al. 2016. Decision Quality: Value Creation From Better Business Decisions. John Wiley & Sons. New Jersey, USA.

[53]

Ralph L. Keeney. 1996. Value-Focused Thinking. Harvard University Press. Massachusetts, USA.

[54]

Robert C. Solomon. 2003. What is an Emotion?: Classic and Contemporary Readings. (2nd Ed.). Oxford University Press. New York, USA.

[55]

Michael Lewis et al. (Eds.). 2010. Handbook of Emotions. Guilford Press. New York, USA.

[56]

Ana Paiva. 1999. Affective Interactions: Toward a New Generation of Computer Interfaces. International Workshop on Affective Interactions. Springer. Berlin, Germany, 1–18

[57]

Philip Gorman. 2004. Motivation and Emotion. Routledge. London, UK.

[58]

David G. Myers et al. 2018. Psychology. Worth Publishers. New York, USA, 459.

[59]

Roger R. Hock. 2015. Forty Studies that Changed Psychology: Explorations into the History of Psychological Research. (7^th Ed.). Prentice-Hall, Inc. England. UK.

[60]

Daniel Cervone and Lawrence A. Pervin. 2015. Personality: Theory and Research. (12^th Ed.). John Wiley & Sons. Danvers, MA, USA, 327–328.

[61]

Robert O. Bucholz and Joseph P. Ward. 2012. London: A Social and Cultural History, 1550–1750. Cambridge University Press. (2012).

[62]

Robert Jervis. 2014. Understanding Beliefs. MIT Press. Massachusetts, USA

[63]

Robert Jervis. 2006. Understanding Beliefs. John Wiley & Sons. New Jersey, USA, 641–663.

[64]

Richards J. Heuer. 1999. Psychology of Intelligence Analysis. Center for the Study of Intelligence. Pittsburg, PA, USA.

[65]

Benedetto De Martino et al. 2006. Frames, biases, and rational decision-making in the human brain. Science (2006), 684–687. https://DOI:10.1126/science.1128356

[66]

Patrick Grim et al. 1998. The Philosophical Computer: Exploratory Essays in Philosophical Computer Modelling. MIT Press. Massachusetts, USA.

[67]

Robert Audi. 2009. The Good in the Right. Princeton University Press. New Jersey, USA.

[68]

Jeremy Bentham. 2015. Introduction to the Principles of Morals and Legislation. Dover Publication, Inc. New York, USA.

[69]

Herbert A. Simon. 1956. Rational choice and the structure of the environment. Psychological Review (1956), 129.

[70]

Reinhard Selten. 1990. Bounded rationality. Journal of Institutional and Theoretical Economics (JITE)/Zeitschrift für die gesamte Staatswissenschaft (Dec. 1990), 649–658. http://www.jstor.org/stable/40751353

[71]

Daniel Kahneman and Amos Tversky. 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, 99–127

[72]

Thomas Atwater. 1992. Thinking critically about ethical issues. Teaching Philosophy (Dec. 1992), 390–393.

[73]

Gordon Graham. 2004. Eight Theories of Ethics. Routledge. New York, USA.

[74]

Mark S. Schwartz. 2016. Ethical decision-making theory: An integrated approach. Journal of Business Ethics (2016), 755–776.

[75]

Ann E. Tenbrunsel and Kristin Smith-Crowe. 2008. 13 ethical decision making: Where We've Been and Where We're Going. Academy of Management Annals (2008), 545–607.

[76]

Peter Singer (Ed.). 2013. A Companion to Ethics. John Wiley & Sons. New Jersey, USA.

[77]

Peter C. Fishburn. 2013. The Foundations of Expected Utility. Springer Science & Business Media. Berlin, Germany.

[78]

John Winsor Pratt et al. 1995. Introduction to Statistical Decision Theory. MIT Press. Massachusetts, USA.

[79]

George F. Loewenstein. 1998. Frames of mind in intertemporal choice. Management Science (Feb. 1988), 200–214.

[80]

Daniel Read et al. 2003. Intertemporal Choice. London School of Economics and Political Science. Working Paper LSEOR 03.58.

[81]

William Samuelson and Richard Zeckhauser. 1988. Status quo bias in decision making. Journal of Risk and Uncertainty (1988), 7–59.

[82]

Eric J. Johnson and Daniel Goldstein. 2003. Do defaults save lives? Science (2003), 1338–1339. DOI:https://doi.org/10.1126/science.1091721

[83]

Jane Burka and Lenora M. Yuen. 2007. Procrastination: Why you do it, what to do about it now. Da Capo Lifelong Books. Cambridge, MA, USA.

[84]

Shane Frederick et al. 2009. Opportunity cost neglect. Journal of Consumer Research (2009), 553–561.

[85]

Ibn Khaldun. 2015. The Muqaddimah. An Introduction to History, Princeton Classics. New Jersey, USA.

[86]

Amos Tversky and Daniel Kahneman. 1991. Loss aversion in riskless choice: A reference-dependent model. The Quarterly Journal of Economics (1991), 1039–1061.

[87]

Bas Verplanken et al. 2005. The Measurement of Habit. The Routines of Decision Making. Lawrence Erlbaum Associates, Publishers. New Jersey, USA, 231–247.

[88]

Judith A. Ouellette and Wendy Wood. 1998. Habit and intention in everyday life: The multiple processes by which past behaviour predicts future behaviour. Psychological Bulletin (1998), 54.

[89]

Daniel Kahneman. 2011. Thinking, Fast and Slow. Macmillan. New York, USA.

[90]

Ramanathan Sugumaran and John Degroote. 2011. Spatial Decision Support Systems: Principles and Practices. CRC Press. Boca Raton, FL, USA.

[91]

Steven Johnson. 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.

[92]

Michel Peeters et al. 2013. Social stairs: Taking the piano staircase towards long-term behavioural change. International Conference on Persuasive Technology. Springer. Berlin. 174–179.

Digital Library

[93]

Harvey J. Miller. 2004. Tobler's first law and spatial analysis. Annals of the Association of American Geographers (2004), 284–289.

[94]

Richard H. Thaler and Cass R. Sunstein. 2008. Nudge: Improving Decisions About Health, Wealth, and Happiness, Yale University Press. London, UK.

[95]

Makoto Naruse et al. 2018. Why is the Environment Important for Decision Making? Local Reservoir Model for Choice-Based Learning. PlOS One. (2018).

[96]

KwangSoo Yang and Shashi Shekhar. 2017. Spatial Network big Databases: Queries and Storage Methods. Springer. Boca Raton, FL, USA.

[97]

Marc Barthélemy. 2011. Spatial networks. Physics Reports (2011).

[98]

Duncan J. Watts. 1999. Small Worlds: The Dynamics of Networks Between Order and Randomness. Princeton: Princeton University Press. Oxfordshire, UK.

Digital Library

[99]

Dev Oliver. 2016. Spatial Network Data: Concepts and Techniques for Summarization. Springer International Publishing. Redlands CA. USA.

[100]

Oana Bucur 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=10.1.1.142.8390

[101]

Maarten Voors 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.

[102]

Charniak Eugene. 1997. Statistical parsing with a context-free grammar and word statistics. AAAI/IAAI (1997), 598–603. https://dl.acm.org/doi/10.5555/1867406.1867499

[103]

Dina Berkeley et al. (Eds.). 1998. Context-Sensitive Decision Support Systems. Springer. Boston, MA, USA, 24–40.

[104]

Alexander Smirnov et al. 2005. Constraint-driven methodology for context-based decision support. Journal of Decision Systems (Jan. 2005), 279–301.

[105]

Jonathan Steinberg. 2011. Bismarck: A Life. OUP. Oxford, UK.

[106]

Jay R. Galbraith. 2014. Designing Organizations: Strategy, Structure, and Process at the Business Unit and Enterprise Levels. John Wiley & Sons. San Francisco, CA, USA.

[107]

Gregory S. Parnell et al. 2013. Handbook of Decision Analysis. John Wiley & Sons; San Francisco, CA, USA.

[108]

Mahen Tampoe. 2012. Strategic Management: Process, Content, and Implementation. Oxford University Press. Oxford, UK.

[109]

Didederik M. Roijers and Shimon Whiteson. 2017. Multi-objective decision making. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool. Austin, Texas, USA, 1–29.

[110]

Michael E. Porter. 2008. The five competitive forces that shape strategy. Harvard Business Review (2008), 25–40. https://hbr.org/1979/03/how-competitive-forces-shape-strategy.

[111]

Norbert M. Seel (Ed.). 2011. Encyclopedia of the Sciences of Learning. Springer Science & Business Media. Berlin, Germany.

[112]

David H. Jonassen. 2000. Toward a design theory of problem-solving. Educational Technology Research and Development 48, 4 (2000), 63–85.

[113]

Edward Grant. 1996. The Foundations of Modern Science in the Middle Ages: Their Religious, Institutional and Intellectual Contexts. Cambridge University Press. New York, USA.

[114]

Daniel A. Levinthal. 1997. Adaptation on rugged landscapes. Management Science (1997), 934–950.

[115]

Stuart Kauffman and Simon Levin. 1987. Towards a general theory of adaptive walks on rugged landscapes. Journal of Theoretical Biology (1987), 11–45.

[116]

Bill McKelvey. 1999. Avoiding complexity catastrophe in coevolutionary pockets: Strategies for rugged landscapes. Organization Science (Jun. 1999), 294–321.

Digital Library

[117]

Scott E. Page. 2010. Diversity and Complexity. Vol. 2. Princeton University Press. New Jersey, USA.

[118]

Gary Klein et al. 2008. Naturalistic decision making. Human Factors (2008), 456–460. https://doi.org/10.1518%2F001872008X288385

[119]

Christopher K. Hauser and Emilio Salinas. 2014. Perceptual Decision Making (2014), 1–21.

[120]

E. Bruce Goldstein and Laura Cacciamani. 2022. Sensation and Perception. Cengage Learning. Boston, MA, USA.

[121]

Itamar Gati et al. 1996. A taxonomy of difficulties in career decision making. Journal of Counseling Psychology (1996), 510. https://psycnet.apa.org/doi/10.1037/0022-0167.43.4.510

[122]

Yee Mey Goh et al. 2020. A variability taxonomy to support automation decision-making for manufacturing processes. Production Planning & Control (2020), 383–399.

[123]

Michele D. Bunn. 1993. Taxonomy of buying decision approaches. Journal of Marketing (1993), 38–56.

[124]

Petra Gibcus 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.

[125]

Howard S. Barrows. 1986. A taxonomy of problem-based learning methods. Medical Education (1986), 481–486.

[126]

Mairéad Hogan et al. 2014. Theorising and testing a taxonomy of decision constructs. Journal of Customer Behaviour (2014), 171–185.

[127]

Melanie K. Johnson et al. 2022. Ethical decision-making models: A taxonomy of models and review of issues. Ethics & Behavior (2021), 1–6.

[128]

Caesar Wu et al. 2019. Value-based cloud price modelling for segmented business to business market (2019), 502–523.

Digital Library

[129]

Rajiv Sabherwal and William R. King. 1995. An empirical taxonomy of the decision-making processes concerning strategic applications of information systems. Journal of Management Information Systems (1995), 177–214.

Digital Library

[130]

Daniel Dennett. 2007. Instead of a review. Artificial Intelligence (2007), 1110–1113. https://ase.tufts.edu/cogstud/dennett/papers/reviewofHofstadter&Minsky.pdf.

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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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cover image ACM Computing Surveys

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: 02 March 2023

Online AM: 24 November 2022

Accepted: 06 November 2022

Revised: 04 November 2022

Received: 26 July 2021

Published in CSUR Volume 55, Issue 12

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https://doi.org/10.1016/j.eswa.2024.125907
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https://doi.org/10.2139/ssrn.4682715
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