Decision making reflecting the fractalization of the society

  • Jan Kalina The Czech Academy of Sciences, Institute of Computer Science
Keywords: decision support, economic equilibrium, management, credit risk, information theory, chaos theory

Abstract


Although the mainstream economic doctrine relies on the concept of equilibrium, the current society has been recently facing serious challenges. While we can experience a gradually rise of the ideals of the knowledge society, we hold the opinion that the society (and the economies worldwide as well) will have a fractal structure following models investigated by the chaos theory. This paper is focused on decision making especially in economic or managerial tasks and its transforms due to the paradigm shift towards a fractal society in disequilibrium economic conditions. Statistical and information-theoretical aspects of decision support are discussed and a decision making example from the field of credit risk management is analyzed and presented.

Author Biography

Jan Kalina, The Czech Academy of Sciences, Institute of Computer Science

Department of Machine Learning

researcher

References

Arrow, K.J. & Debreu, G. (1954). Existence of an equilibrium for a competitive economy. Econometrica, 22, 265–290.
Biais, B., Foucault, T., & Moinas, S. (2015). Equilibrium fast trading. Journal of Financial Economics, 116, 292–313.
Briggs, J. (2015). Fractals: The Patterns of Chaos. Discovering a New Aesthetic of Art, Science, and Nature. Brattleboro: Echo Point Book & Media.
Brownstein, N.C., Louis, T.A., O’Hagan, A., & Pendergast, J. (2019). The role of expert judgment in statistical inference and evidence-based decision-making. American Statistician, 73, 56–68.
Chen, F., Tian, K., Ding, X., Li, T., Miao, Y., & Lu, C. (2017). Multifractal characteristics in maritime economics volatility. International Journal of Transport Economics, 44, 365–380.
Cialowicz, B. (2017). Demand sphere as a co-engine of sustainable development. European Journal of Sustainable Development, 6 (4), 465–474.
Dimson, E., Marsh, P., & Staunton, M. (2017). Factor-based investing: The long-term evidence. Journal of Portfolio Management, 43 (5), 15–37.
Dua, D., & Graff, C. (2017). UCI machine learning repository, available at [http:// archive.ics.uci. edu/ml].
Düppe, T., & Weintraub, E.R. (2016). Losing equilibrium: On the existence of Abraham Wald’s fixed-point proof of 1935. History of Political Economy, 48, 635–655.
Faggini, M., Bruno, B., & Parziale, A. (2019). Does chaos matter in financial time series analysis? International Journal of Economics and Financial Issues, 9 (4), 18–24.
Harrell, F.E. (2015). Regression Modeling Strategies with Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 2nd end. Cham: Springer.
von Heusinger, A., Kanzow, C., & Fukushima, M. (2012). Newton’s method for computing a normalized equilibrium in the generalized Nash game through fixed point formulation. Mathematical Programming, 132, 99–123.
Holt, C.A., & Roth, A.E. (2004). The Nash equilibrium: A perspective. Proceedings of the National Academy of Sciences, 101, 3999–4002.
Ignatius, J., Hatami-Marbini, A., Rahman, A., Dhamotharan, L., & Khoshnevis, P. (2018). A fuzzy decision support system for credit scoring. Neural Computing and Applications, 29, 921–937.
Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.
Kalina, J. (2013). Highly robust methods in data mining. Serbian Journal of Management, 8 (1), 9–24.
Kalina, J. (2014). On robust information extraction from high-dimensional data. Serbian Journal of Management, 9 (1), 131–144.
Kalina, J. (2015). Three contributions to robust regression diagnostics. Journal of Applied Mathematics, Statistics and Informatics, 11 (2), 69–78.
Kalina, J. (2017). High-dimensional data in economics and their (robust) analysis. Serbian Journal of Management, 12 (1), 157–169.
Kalina, J., & Duintjer Tebbens, J. (2015). Algorithms for regularized linear discriminant analysis. In Proceedings of the 6th International Conference on Bioinformatic Models, Methods and Algorithms (Bioinformatics ’15), Lisbon: Scitepress, pp. 128–133.
Kalina, J., & Schlenker, A. (2015). A robust supervised variable selection for noisy high-dimensional data. BioMed Research International, 2015, 320385.
Kalina, J., & Tichavský, J. (2020). On robust estimation of error variance in (highly) robust regression. Measurement Science Review, 20, 6–14.
Kalina, J., Vašaničová, P., & Litavcová, E. (2019). Regression quantiles under heteroscedasticity and multicollinearity: Analysis of travel and tourism competitiveness. Ekonomický časopis/Journal of Economics, 67, 69-85.
Kalina, J., & Vidnerová, P. (2020). Regression neural networks with a highly robust loss function. In Analytical Methods in Statistics, Maciak, M. et al., Eds. Springer Proceedings in Mathematics & Statistics, 329, 17–29.
Kalina, J., & Zvárová, J. (2013). Decision support systems in the process of improving patient safety. In E-health Technologies and Improving Patient Safety: Exploring Organizational Factors, A. Moumtzoglou and A. Kastania, Eds. Hershey: IGI Global, 71–83.
Klioutchnikov, I., Sigova, M., & Beizerov, N. (2017). Chaos theory in finance. Procedia Computer Science, 119, 368–375.
Lahmiri, S., & Bekiros, S. (2020). Big data analytics using multi-fractal wavelet leaders in high-frequency Bitcoin markets. Chaos, Solitons & Fractals, 131, 109472.
Liu, Z., Ye, Y., Ma, F., & Liu, J. (2017). Can economic policy uncertainty help to forecast the volatility: A multifractal perspective. Physica A, 482, 181–188.
Luo, C. (2020). A comprehensive decision support approach for credit scoring. Industrial Management & Data Systems, 120, 280-290.
Móczár, J. (2020). The Arrrow-Debreu model of general equilibrium and Kornai's critique in the light of neoclassical economics. Journal of Banking, Finance and Sustainable Development, 1, in press.
Moreno-Jiménez, J.M., & Vargas, L.G. (2018). Cognitive multiple criteria decision making and the legacy of the analytic hierarchy process. Estudios de Economía Aplicada, 36, 67–80.
Outrata, J.V., Ferris, M.C., Červinka, M., & Outrata, M. (2016). On Cournot-Nash-Walras equilibria and their computation. Set-valued and Variational Analysis, 24, 387–402.
Redko, V.G., & Sokhova, Z.B. (2017). Processes of self-organization in the community of investors and producers. Studies in Computational Intelligence, 736, 163–169.
Samuelson, L. (2016). Game theory in economics and beyond. Journal of Economic Perspectives, 30 (4), 107–130.
Sandubete, J.E., & Escot, L. (2021). DChaos: Chaotic time series analysis. R package version 0.1-6 [https://CRAN.R-project.org/package=DChaos].
Silva
, S.T., Mota, I., & Grilo, F. (2013). The use of game theory in regional economics: A quantitative retrospective. Papers in Regional Science, 94, 421–441.
Tetlock, P.E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. New York: Crown Publishers.
Večeř, J. (2019). Dynamic scoring: Probabilistic model selection based on utility maximization. Entropy, 21 (1), 36.
Wakode, S. (2020). Efficacious scrutinizing of COVID-19 impact on banking using credit risk metrics. International Journal of Finance & Economics, 6 (3), 51–56.
Witzany, J. (2017). Credit risk management. Pricing, Measurement, and Modeling. Cham: Springer.

Published
2022/03/31
Section
Original Scientific Paper