Abstract
A decision-making process is a part of the decision-making theory, reasonably placing a major research interest on the question how the process is conducted and what affects the process itself in general. Naturally it is perceived as a sequence of steps, where things are moving forward little-by-little towards to the settled goal. An analysis could be done before (planning), during the process (control + adaption) or afterwards (analysis and evaluation). Also, we can just study someone’s decision process first, mainly trying to avoid making “their” mistakes. Anyway, making decisions or just observing and studying them is a part of life. Either one assumes evaluation of the current situation and of the expected outcomes, assigning to each decision some “quality” according to the fixed set of criteria (like probabilistic), or the flexible ones (different heuristics). Thus, from the mathematical and the philosophic points of view we will face three principle questions applicable to any particular decision-making theory: (1) How many criteria do we need? (2) How well they are defined/described? (3) Are there any relations between them, or we can consider them to be independent ones? Besides, any admissible theory also will consider some kind of underground efficiency questions (at least not to over-complicate and postpone a decision-making process), possibility to track and secure the major and intermediate goals and et cetera. It is clear that theoretical research and even the hated ad-hoc hypothesis use some reasonable assumptions about criteria selection and their quantity: pure or context oriented, but we want to consider the presented problem without restrictions of any specific theory, domain or context; using just common sense and analogies between exact and human sciences detected in twentieth century an later. Therefore, we created a hypothesis on how many evaluation criteria do we really need to operate inside an abstract decision domain—regardless the nature of criteria and their relations with real-world processes. Actually, it was not a big surprise that it resulted to be related with concepts of fractals, chaos and the notion of the fractal dimension. Their clear presence was discovered in many social and biological sciences recently, so an investigation was continued not only in terms of finding “deep” arguments to prove our postulates: recent results in math and physics also showed that most dynamic processes could be described differently considering an analysis of the current situation, short-term and long-term runs. Hence, the nature and the quantity of the involved criteria may vary (they could be implicitly time-dependent) and we need to study this kind of relation also.
Similar content being viewed by others
References
Bak, P. (1996). How nature works. New York: Springer.
Bossert, T. (1998). Analyzing the decentralisation of health systems in developing countries: Decision space, innovation and performance. Social Science & Medicine, 47, 513–1527 47(10):1.
Brehmer, B. (1992). Dynamic decision making: Human control of complex systems. Acta Psychologica, 81, 211–241.
Brim, O.G. Jr., Glass, D.C. & Lavin, D.B. (1962) Personality and Decision Process: Studies in the Social Psychology of Thinking. Stanford, California: Stanford University Press.
Daro, P., Mosher, F.A., Corcoran, T. (2011). Learning trajectories in mathematics: a foundation for standards, curriculum, assessment and instruction. CPRE research report # RR-68. https://repository.upenn.edu/cgi/viewcontent.cgi?article=1019&context=cpre_researchreports.
Davies, N. H. (2015). Fractal dimension (df) as a new structural biomarker of clot microstructure. Thrombosis and Haemostasis, 114(6), 1251–1259.
De Houwer, J., & Hermans, D. (2010). Cognition and emotion: Reviews of current research and theories. New York: Taylor & Francis Retrieved from https://www.uv.mx/rmipe/files/2017/12/cognition_and_emotion.pdf.
Doukhan, P. T. (2002). Long-range dependence. Boston: Birkhäuser.
Evertsz, R., Thangarajah, J., Ly, T. (2019). Practical modelling of dynamic decision making. Springer. https://doi.org/10.1007/978-3-319-95195-9.
Falconer, K. (2014). The geometry of fractal sets (3rds ed.). Hoboken: Wiley.
Fotion, N. G. (1990). Military ethics: Looking toward the future. Stanford: Hoover Institution Press.
George, J., & Dane, E. (2016). Affect, emotion, and decision making. Organizational Behavior and Human Decision Processes, 136, 47–55. https://doi.org/10.1016/j.obhdp.2016.06.004.
Grabisch, M. (1996). The application of fuzzy integrals in multicriteria decision making. European Journal of Operational Research, 89, 445–456. https://doi.org/10.1016/0377-2217(95)00176-x.
Hansson, S. O. (2005). Decision Theory: A Brief Introduction. https://web.archive.org/web/20060705052730/http://www.infra.kth.se/~soh/decisiontheory.pdf.
Hansson, S. O. (2011). Decision Theory: An Overview. In: Lovric M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg, pp.349-355. https://doi.org/10.1007/978-3-642-04898-2_22.
Isen, A. M., & Shalker, T. E. (1982). The effect of feeling state on evaluation of positive, neutral, and negative stimuli: When you “accentuate the positive,” do you “eliminate the negative”? Social Psychology Quarterly, 45, 58–63.
Lawler, G.F., Schramm, O., & Werner, W. (2000). The Dimension of the Planar Brownian Frontier is 4/3. Cornell University Library. p. 15. https://arxiv.org/pdf/math/0010165.pdf.
Lerman, S. (Ed.). (2014). Encyclopedia of Mathematics Education. Springer. https://doi.org/10.1007/978-94-007-4978-8.
Lerner, J., Li, Y., Valdesolo, P., & Kassam, K. (2015). Emotion and Decision Making. Annual Review of Psychology, 66(1), 799–823. https://doi.org/10.1146/annurev-psych-010213-115043.
Liu, J. Z.-K.-B.-F. (2017). Decision process in MCDM with large number of criteria and heterogeneous risk preferences. Operations Research Perspectives, 4, 106–112. https://doi.org/10.1016/j.orp.2017.07.001.
Mardani, A., Jusoh, A., Nor, K.Md., Khalifah, Z., Zakuan, N., & Valipour, A. (2015). Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014. Economic Research-Ekonomska Istraživanja, 28(1), 516–571.
Mattei, T. (2014). Unveiling complexity: Non-linear and fractal analysis in neuroscience and cognitive psychology. Frontiers in Computational Neuroscience, 8, 1–2. https://doi.org/10.3389/fncom.2014.00017.
Rabinovich, M. I., Huerta, R., Varona, P., & Afraimovich, V. S. (2008). Transient cognitive dynamics, metastability and decision making. PLoS Comput Biol, 4, e1000072. https://doi.org/10.1371/journal.pcbi.1000072.
Rényi, A. (1959, March). On the dimension and entropy of probability distributions. Acta Mathematica Academiae Scientiarum Hungaricae, 10(1–2), 193–215. https://doi.org/10.1007/BF02063299.
Samorodnitsky, G. (2016). Stochastic processes and long range dependence. Cham: Springer International Publishing.
Simon, H. (1978). Rationality as process and as product of thought. Richard T.Ely lecture. American Economic Review, 68, 1–16.
Simon, M. (1995). Reconstructing mathematics pedagogy from a constructivist perspective. Journal for Research in Mathematics Education, 26, 114–145.
Solé, R. V., & Goodwin, B. C. (2001). Signs of life: How complexity pervades biology. New York: Basic Books.
Stewart, T. (1981). A descriptive approach to multiple-criteria decision making. The Journal of the Operational Research Society, 32(1), 45. https://doi.org/10.2307/2581468.
Toda, M. (1980). Emotion and decision making. Acta Psychologica, 45(1–3), 133–155. https://doi.org/10.1016/0001-6918(80)90026-8.
Turk-Browne, N. B. (2013). Functional interactions as big data in the human brain. Science, 342, 580–584. https://doi.org/10.1126/science.1238409.
Usui, H. (2003). Rheological characteristics of non-spherical graphite suspensions. Korea-Australia rheology journal, 15, 19–25.
Wagenmakers, E.-J., Farrell, S., & Ratcliff, R. (2005). Human cognition and a pile of sand: A discussion on serial correlations and self-organized criticality. Journal of Experimental Psychology: General, 135, 108–116.
Yager, R., & Alajlan, N. (2015). Fuzzy measures in multi-criteria decision making. Procedia Computer Science, 62, 107–115. https://doi.org/10.1016/j.procs.2015.08.421.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Dulov, E. Evaluation of Decision-Making Chains and their Fractal Dimensions. Integr. psych. behav. 55, 386–429 (2021). https://doi.org/10.1007/s12124-020-09566-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12124-020-09566-9