Abstract

The study of decision-making is an intellectual discipline; mathematics, sociology, psychology, economics, political science, artificial intelligence, neuroscience and physics. Conventional decision theory tells us what choice of behavior should be made if we follow certain axioms. Scientific curiosity instructs us to reconsider beyond any area in which we have defined ourselves. We design the intertwining of brain, genetics, phylogenetics, and artificial and neural networks in financial trading to find the best combinations of parameter values in financial trading, incorporating them into ANN models for stock selection and trader identification. The purpose and goal of the paper is to make financial decisions in the intertwining of the brain, genetics, phylogenetics and artificial neural networks, focusing on opening new foundations, giving insights into the foundation rock that lies beneath that soil. Science seeks basic natural laws. Mathematics seeks new theorems to build on old ones. Engineering builds systems to address human needs. The three disciplines are interdependent, but different and yet Claude Shannon simultaneously makes a central contribution to all three disciplines, this was the guiding idea of our work (finance, neuroscience, artificial intelligence).

Keywords: financial decision making, brain, genetics, phylogenetics, neural and quantum networks.

JEL Classification: A19, O16, O33, G41.

Cite as: Njegovanović, A. (2021). How Do We Decide? Thought Architecture Decision Making?. Financial Markets, Institutions and Risks, 5(2), 58-71. https://doi.org/10.21272/fmir.5(2).58-71.2021

This work is licensed under a Creative Commons Attribution 4.0 International License

References

1. Aerts, D. (2002). Being and change: Foundations of a realistic operational formalism. In D. Aerts, M. Czachor, & T. Durt (Eds.), Probing the structure of quantum mechanics: Nonlinearity, nonlocality, probability and axiomatics. Singapore: World Scientific, 71–110. [Link]
2. Aerts, D. (2009). Quantum structures in cognition. Journal of Mathematical Psychology, 53, 314–348. [CrossRef]
3. Aerts, D., & Aerts, S. (1995). Applications of quantum statistics in psychological studies of decision processes. Foundations of Science, 1, 85-97. [CrossRef]
4. Andersen, R. A. (1995). Encoding of intention and spatial location in the posterior parietal cortex. Cereb. Cortex, 5, 457–469. [CrossRef]
5. Anderson, P. W. (1972). More is different. Science 177, 393–396. [Google Scholar]
6. Aziz R, Verma C, Srivastava N (2018) Artificial neural network classification of high dimensional data with novel optimization approach of dimension reduction. Ann Data Sci 5(4):615–635. [Google Scholar]
7. Blackburn, S. (1999). Think. Oxford: Oxford University Press. ISBN: 9780192100245
8. Bogacz, R., Brown, E., Moehlis, J., Holmes, P., and Cohen, J. D. (2006). The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Psychol. Rev. 113, 700–765. [Google Scholar]
9. Bok, H. (1998). Freedom and Responsibility. Princeton: Princeton University Press. [Google Scholar]
10. Born, R. T., and Bradley, D. C. (2005). Structure and function of visual area MT. Annu. Rev. Neurosci. 28, 157–189. [Google Scholar]
11. Braga AP, Carvalho ACPLF, Ludermir TB (2000) Redes neurais artificiais: teoria e aplicações, 2nd edn. Editora, Rio de Janeiro. [Link]
12. Britten, K. H., Shadlen, M. N., Newsome, W. T., and Movshon, J. A. (1992). The analysis of visual motion: a comparison of neuronal and psychophysical performance. J. Neurosci. 12, 4745–4765. [Google Scholar]
13. Chen, X., Gao, P. (2019). Path planning and control of soccer robot based on genetic algorithm. J Ambient Intell Human Comput. [CrossRef]
14. Chen, JiaWang, et al. (2008). Research on fuzzy control of path tracking for underwater vehicle based on genetic algorithm optimization. Ocean Engineering 156, 217–223. [Google Scholar]
15. Cooper JC (1999) Artificial neural networks versus multivariate statistics: an application from economics. J Appl Stat 26(8):909–921. [CrossRef]
16. Colby, C. L., and Goldberg, M. E. (1999). Space and attention in parietal cortex. Annu. Rev. Neurosci. 22, 319–349. [Google Scholar]
17. Crick, F. (1994). The Astonishing Hypothesis: The Scientific Search for the Soul. New York: Charles Scribner’s Sons. [Link]
18. Deaner, R. O., Khera, A. V., and Platt, M. L. (2005). Monkeys pay per view: adaptive valuation of social images by rhesus macaques. Curr. Biol. 15, 543–548. [Google Scholar]
19. Dennett, D. (1984). I could not have done otherwise – so what. J. Philos. 81, 553–567. [Link]
20. Ditterich, J., Mazurek, M., and Shadlen, M. N. (2003). Microstimulation of visual cortex affects the speed of perceptual decisions. Nat. Neurosci. 6, 891–898. [CrossRef]
21. Drugowitsch, J., Moreno-Bote, R., Churchland, A. K., Shadlen, M. N., and Pouget, A. (2012). The cost of accumulating evidence in perceptual decision making. J. Neurosci. 32, 3612–3628. [Google Scholar]
22. Faisal, A. A., Selen, L. P., and Wolpert, D. M. (2008). Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303. [Google Scholar]
23. Frankfurt, H. G. (1971). Freedom of the will and the concept of a person. J. Philos. 68, 5–21. [Google Scholar]
24. Gazzaniga, M. S. (2011). Who’s in Charge? New York: Harper Collins. [Google Scholar]
25. Lo Bosco. (2001). A genetic algorithm for image segmentation, Proceedings 11th International Conference on Image Analysis and Processing, Palermo, 262–266.
26. Glimcher, P. (2003). Decisions, Uncertainty, and the Brain: The Science of Neuroeconomics. Cambridge, MA: MIT Press. ISBN 0-262-07244-0. [Google Scholar]
27. Gold, J. I., and Shadlen, M. N. (2002). Banburismus and the brain: decoding the relationship between sensory stimuli, decisions, and reward. Neuron 36, 299–308. [CrossRef]
28. Gold, J. I., and Shadlen, M. N. (2007). The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574. [Google Scholar]
29. Greene, J., and Cohen, J. (2004). For the law, neuroscience changes nothing and everything. Philos. Trans. R. Soc. Lond. B Biol. Sci. 359, 1775–1785. [Google Scholar]
30. Hanks, T. D., Ditterich, J., and Shadlen, M. N. (2006). Microstimulation of macaque area LIP affects decision-making in a motion discrimination task. Nat. Neurosci. 9, 682–689. [Google Scholar]
31. Hanks, T. D., Kiani, R., and Shadlen, M. N. (2009). A neural correlate of the tradeoff between the speed and accuracy of a decision, in Neuroscience Meeting Planner (Chicago, IL: Society for Neuroscience). [CrossRef]
32. Hanks, T. D., Mazurek, M. E., Kiani, R., Hopp, E., and Shadlen, M. N. (2011). Elapsed decision time affects the weighting of prior probability in a perceptual decision task. J. Neurosci. 31, 6339–6352. [CrossRef]
33. Huang, J., You, X. Y., Liu, H. C., & Si, S. L. (2019). New approach for quality function deployment based on proportional hesitant fuzzy linguistic term sets and prospect theory. International Journal of Production Research, 57(5), 1283–1299. [Google Scholar]
34. Khashei M, Bijari M (2010) An artificial neural network (p, d, q) model for timeseries forecasting. Expert Syst Appl 37(1):479–489. [CrossRef]
35. Mohammed, Mazin Abed, et al. (2017). Solving vehicle routing problem by using improved genetic algorithm for optimal solution. Journal of computational science, 21, 255–262. [Google Scholar]
36. Olson DL, Shi Y, Shi Y (2007) Introduction to business data mining, vol 10. McGraw-Hill/Irwin Englewood Cliffs, New York. ISBN 0-02-389340-0. [Google Scholar]
37. Pang, Q., Wang, H., & Xu, Z. S. (2016). Probabilistic linguistic term sets in multi-attribute group decision making. Information Sciences, 369, 128–143. [CrossRef]
38. Podsiadlo M, Rybinski, H. (2016). Financial time series forecasting using rough sets with time-weighted rule voting. Expert Syst Appl 66:219-233. [Google Scholar]
39. Rath, Asita Kumar, et al. (2019). Path optimization for navigation of a humanoid robot using hybridized fuzzy-genetic algorithm. International Journal of Intelligent Unmanned Systems. [Google Scholar]
40. Ren, P. J., Hao, Z. N., Zeng, X., & Xu, Z. S. (2020). Decision models based on incomplete hesitant fuzzy linguistic preference relation with application to site section of hydropower stations. IEEE Transcations on Engineering Management. IEEE Transactions on Engineering Management. [Link]
41. Saini, A., Sharma, A. (2019). Predicting the unpredictable: an application of machine learning algorithms in Indian stock market. Ann Data Sci 6, 1–9. [Google Scholar]
42. Shi, Y., Tian Y., Kou, G., Peng, Y., Li, J. (2011). Optimization based data mining: theory and applications. Springer, Berlin. [Google Scholar]
43. Shi, Y. (2014). Big data: history, current status, and challenges going forward. Bridge, 44(4), 6–11. [Google Scholar]
44. Tkáč M, Verner, R. (2016). Artificial neural networks in business: two decades of research. Appl Soft Comput 38:788–804. [CrossRef]
45. Xu, Z., Shi, Y. (2015). Exploring big data analysis: fundamental scientific problems. Ann Data Sci, 2(4), 363–372. [Google Scholar]
46. Wang, J., et al. (2016). Multi-offspring genetic algorithm and its application to the traveling salesman problem. Applied Soft Computing 43 415–423. [CroosRef]