Decision making aided by Artificial Intelligence

Rafał Łabędzki

Abstract

The aim of the paper is to present how machine learning can support managers in the decision-making process. Analysis of the issue presented in this elaboration includes a review of the relevant literature on decision theory and utility as well as machine learning methods. The paper discusses possibilities of supporting managers through artificial intelligence in the context of stages of the decision-making process. It has been demonstrated that - thanks to artificial intelligence - it is possible to better estimate the expected utility of the alternatives that decision-maker choose from. Additionally, the paper presents an argument indicating that the use of machine learning makes the manager's decision-making process closer to the normative approach.

Keywords: decision-making process, artificial intelligence, utility, machine learning, management

References

• Arrow, K. J. (1951). Alternative approaches to the theory of choice in risk-taking situations. Econometrica, 19(4), 404-437. https://doi.org/10.2307/1907465
• Bentham, J. (1789). An introduction to the principles of morals and legislation. T. Payne and Son.
• Bernoulli, D. (1954). Exposition of a New Theory on the Measurement of Risk. Econometrica, 22(1), 23-36. https://doi.org/10.2307/1909829
• Boschetti, A., & Massaron, L. (2015). Python data science essentials. Packt Publishing Ltd. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. The MIT Press, 19, 305-307. https://doi.org/10.1007/s10710-017-9314-z
• Holska, A. (2016). Teorie podejmowania decyzji. In: K. Klincewicz (Ed.), Zarządzanie, organizacje, organizowanie przegląd perspektyw teoretycznych (pp. 239-251). Wydawnictwo Naukowe Wydziału Zarządzania Uniwersytetu Warszawskiego. https://timo.wz.uw.edu.pl/wp-content/uploads/2016/09/Klincewicz-Krzysztof-red-Zarzadzanie-organizacje-i-organizowanie.pdf
• Ingram, J. K. (1888). A history of political economy. The Macmillan Company.
• Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292. https://doi.org/10.2307/1914185
• Mill, J. S. (1844). Essays on some unsettled questions of political economy. John W. Parker.
• Murphy, K. P. (2021). Probabilistic Machine Learning: An introduction. MIT Press.
• Natingga, D. (2018). Data Science Algorithms in a Week: Top 7 algorithms for scientific computing, data analysis, and machine learning. Packt Publishing Ltd.
• Newell, A., & Simon, H. A. (1976). Computer science as empirical inquiry: symbols and search. Communications of the ACM, 19(3), 113-126. https://doi.org/10.1145/360018.360022
• Object Management Group. (2023). Decision Model and Notation. https://www.omg.org/spec/DMN/1.5/Beta1/PDF
• Peterson, M. (2023). The St. Petersburg Paradox. The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/archives/fall2023/entries/paradox-stpetersburg
• Raschka, S., & Mirjalili, V. (2019). Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2 (3rd ed.). Packt Publishing Ltd.
• Samuelson, P. A. (1947). Foundations of economic analysis. Harvard University Press.
• Savage, L. J. (1954). The foundations of statistics. John Wiley & Sons.
• Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69(1), 99-118. https://doi.org/10.2307/1884852
• Simon, H. A. (1957). Models of man; social and rational. Wiley.
• Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-466. https://doi.org/10.1093/mind/LIX.236.433
• Von Neumann, J., & Morgenstern, O. (1947). Theory of games and economic behavior (2nd rev. ed.). Princeton University Press.