Abstract

The purpose of this paper is to examine the theoretical interaction of brain dynamics using fractal information tools, fractal geometry in fnancial decision making. This paper concludes a scientific analytical observatory focusing on financial decision making. Through the integration of neuroscien-tific approach to the brain as a fractal and financial decision-making with the concept of fractal fi-nancial market, we open the analytical framework through two interrelated approaches that can in-crease information on making financial decisions and improve effective financial decisions in times of uncertainty. In order to achieve the aim of the work, the concept of “organized business forms” and the analogue of neurons in the financial market is the fractal – the price of a financial asset, to summarize theoretical basics, multifraktal and financial market / fractal trading, fractal computing architecture. Time series of property prices are dental lines, Fractal Computing Architecture.

Financial decision making is a complex system whose analysis requires a holistic approach including scientific branches (from economics, neuroscience, psychology, mathematics) to understanding the complexity of decision making. Making financial decisions is a complex system whose analysis re-quires a holistic approach, including scientific branches (from economics, neuroscience, psychology, maths) to understanding the complexity of decision making. Thus, fractal geometry provides a new epistemological framework for the interpretation of real life and the natural world as it is, thus pre-venting approximation or subjective view. The dynamics of globalization and financial system devel-opment opens up new research findings with growing information and financial tools through the use of scientific tools to address complex financial problems.

Keywords: fractal geometry, fractal information, financial decision making, brain/neural networks.

JEL Classification: G41.

Cite as: Njegovanović, An. (2019). Theoretical Insight in Financial Decision and Brain as Fractal Computer Architecture. Financial Markets, Institutions and Risks, 3(2), 91-101. http://doi.org/10.21272/fmir.3(2).91-101.2019.

References

1. Agnati L. F., Baluska F., Barlow P. W., GGuidolin D. ( 2009). Mosaic. Self- similarity logic, and biological attraction. Commun. Integr. Biol. 2, 552-563.
2. Agnati L. F., Santarossa L., Genedani S., Canela E. I., Leo G., Franco R., Woofs A., Lluis C., Ferre S., Fuxe K. (2004). On the nested hierarchical organization of CNS: basic characteristics of neuronal molecular organization in Cortical Dynamics LNCS 3146 (ed. Erdi P., editor. ), 24–54, Springer, Berlin
3. Albert R., Barabasi A. (2002). Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47–9710.1103/RevModPhys.74.47
4. Andersen C. M. (2000). From molecules to mind: how vertically convergent fractal time fluctuations unify cognition and emotion. Conscious. Emot. 1, 193–22610.1075/ce.1.2.02and
5. Babloyantz A, Louren, C (1994) Computation with Chaos: A paradigm for cortical activity, Proc Natal Acad Sci USA, 91, 9027-9031.
6. Bandyopadhyay, A.; Miki, K. (2013). A vertical parallel processor. JP-5187804.
7. Baraasi, A,-L and Albert, R. (1999). Emergence of scaling random networks. Science 286, 5019-512.
8. Beiser, D.G.; Hua, S.E.; Houk, J.C. (1997). Network models of the basal ganglia. Curr. Opin. Neurobiol., 7, 185–190.
9. Dijkstra, E. (1979). Programming Considered as a Human Activity. Classics in Software Engineering, Yourdon Press: New York.
10. Dorogovtsev S. N., Mendes J. F. F. (2000). Evolution of networks with aging of sites. Phys. Rev. E62, 1842–184510.1103/PhysRevE.62.1842
11. Dorogovtsev S. N., Mendes J. F. F. (2001). Evolution of networks. Adv. Phys. 51, 1079 118710.1080/00018730110112519
12. Dorogovtsev S., Mendes J. (2003). Evolution of Networks: From Biological Nets to the Internet and WWW. New York: Cambridge University Press.
13. Fraiman, D., and Chialvo, D.R. ( 2012). What kind of noise is brain noise: anomalus scaling behavior oft he resting brain activity fluctuations. Front.Physiol. 3:307.
14. Gallos, L.K., Makse, H.A. and Sigman, M. ( 2012 ). A small world of weak ties provides optimal global integration of self-similar module sin functional brain networks. Proc. Natl. Acad. Sci. USA 109, 2825-2830
15. Gardiner J, Marc J, Overall R. (2008). Cytoskeletal thermal ratchets and cytoskeletal tensegrity: determinants of brain asymmetry and symmetry? Front Biosci; 13: 4649-4656.
16. Hilgetag C. C., Koetter R., Stephan K. E., Sporns O. (2002). Computational methods for the analysis of brain connectivity, in Computational Anatomy, ed. Ascoli G. A., editor. (Totowa, NJ: Humana Press; ), 295–335.
17. Hilgetag C. C., Koetter R., Stephan K. E., Sporns O. (2002). Computational methods for the analysis of brain connectivity, in Computational Anatomy, ed. Ascoli G. A., editor. (Totowa, NJ: Humana Press; ), 295–335.
18. Laurientia, P. J., Joyceb, K. E. (2011). Telesfordb, Q. K., Burdettea, J.H. and Hayasaka. S. Universal fractal scaling of self-organized networks. Physical A 390, 3608-3613
19. Losa GA. (2009). The fractal geometry of life. Riv Biol 102(1):29–59
20. Mandelbrot BB. (1975). Stochastic models for the earth’s relief, the shape and the fractal dimension of the coastlines, and the number-area rule for islands. Proc Natl Acad Sci U S A 72(10):3825–8.
21. Mandelbrot BB. (1983). The fractal geometry of nature. New York: W.H. Freeman & Co.
22. Mandelbrot B. B., van Ness J. W. (1968). Fractional Brownian motions, fractional noises and applications. SIAM Rev. 10, 422–43710.1137/1010093
23. Milosević NT, Ristanović D, Gudović R, Rajković K, Marić D. (2007). Application of fractal analysis to neuronal dendritic arborisation patterns of the monkey dentate nucleus. Neuroscience Letters; 425: 23-27.
24. Oku, M.; Aihara, K. (2008). A mathematical model of planning in the prefrontal cortex. Artif. Life Robot., 12, 227–231.
25. Park J. P., Newman M. E. J. (2004). Statistical mechanics of networks. Phys. Rev. E, 70, 066117.10.1103/PhysRevE.70.066117.
26. Prévost, C.; McNamee, D.; Jessup, R.K.; Bossaerts, P.; O’Doherty, J.P. (2013). Evidence for model-based computations in the human amygdala during pavlovian conditioning. PLoS Comput. Biol., 9, e1002918.
27. Roerig B., Chen B. (2002). Relationships of local inhibitory and excitatory circuits to orientation preference maps in Ferret visual cortex. Cereb. Cortex 12, 187–19810.1093/cercor/12.2.187
28. Rozenfeld, H. D.., Song, C. and Makse, H. A. (2010). Small-world to fractal transition in complex networks: a renormalization group approach. Phys. Rev. Lett. 104, 025701.
29. Schmahmann, J. D., Caplan, D. Cognition, emotion and cerebellum. Brain 2006, 129, 290-292.
30. Sandu AL, Rasmussen IA Jr., Lundervold A, Kreuder F, Neckelmann G, Hugdahl K, Specht K. (2007). Fractal dimension analysis of MR images reveals grey matter structure irregularities in schizophrenia. Comput Med Imaging Graph, 32, 150-158.
31. Sporns O., Koetter R. (2004). Motifs in brain networks. PLoS Biol. 2, e369.10.1371/journal.pbio.0020369
32. Stam C. J., de Bruin E. A. (2004). Scale-free dynamics of global functional connectivity in the human brain. Hum. Brain Mapp. 22, 97–10910.1002/hbm.20016
33. Stam C. J., Reijneveld J. C. (2007). Graph theoretical analysis of complex network in the brain. Nonlinear Biomed. Phys, 1, 3.10.1186/1753-4631-1-3
34. Stam C. J. (2005). Nonlinear dynamical analysis of EE and MEG: review of an emerging field. Clin. Neurophys. 116, 2266–230110.1016/j.clinph.2005.06.011
35. Song, C., Havlin, S. and Makse, H.A. ( 2005). Self-similarity of complex networks. Nature 433, 392-395.
36. Song, C., Havlin, S. and Makse, H.A. ( 2006). Origins of fractality int he growth of complex networks. Nat Phys. 2, 275-281.
37. Tian, X.; Xiao, Z.G. (October 2005). Functional Model of Brainstem-Cortex-Thalamus Circuit. In Proceedings of the 2005 Neural International Conference on Networks and Brain, ICNN&B ’05, Beijing, China, 13–15; Volume 3.
38. Tsuda, I.; Yamaguti, Y.; Kuroda, S.; Fukushima, Y.; Tsukada, M. (2008). A mathematical model for the hippocampus: Towards the understanding of episodic memory and imagination. Prog. Theor. Phys. Suppl., 173, 99–108.
39. Wang N, Butler JP, Ingber DE. (1993). Mechanotransduction across the cell surface and through the cytoskeleton. Science; 260: 1124-1127.
40. Werner, G. (2010). Fractals int he nervous system: conceptual implications for theoretical neuroscience. Front. Physiol. 1:15.
41. West B.J. (2010). Fractal physiology and the fractional calculus: a perspective. Front Physiol 1:12.
42. Zanotti, G., Guerra C. (2003). Is tensegrity a unifying concept of protein folds? FEBS Lett; 534: 7-10.