Abstract
The main purposes of econometrics are: to describe economic phenomena, and to find out how to regulate these phenomena to get the best possible results. There have been many successes in both purposes. Companies and countries actively use econometric models in making economic decisions. However, in spite of all the successes of econometrics, most economically important decisions are not based only on the econometric models – they also take into account expert opinions, and it has been shown that these opinions often drastically improve the resulting decisions. Experts – and not econometricians – are still largely in charge of the world economics. Similarly, in many other areas of human activities, ranging from sports to city planning to teaching, in spite of all the successes of mathematical models, experts are still irreplaceable. But why? In this paper, we explain this phenomenon by taking into account that many complex systems are well described by quantum equations, and in quantum physics, the best computational results are obtained when we allow the system to make kind of imprecise queries – the types that experts ask.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baaquie, B.E.: Quantum Finance: Path Integrals and Hamiltonians for Options and Interest Rates. Camridge University Press, New York (2004)
Belohlavek, R., Dauben, J.W., Klir, G.J.: Fuzzy Logic and Mathematics: A Historical Perspective. Oxford University Press, New York (2017)
Buchanan, B.G., Shortliffe, E.H.: Rule Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Reading (1984)
Deutsch, D., Jozsa, R.: Rapid solutions of problems by quantum computation. Proc. R. Soc. Lond. A 439, 553–558 (1992)
Feynman, R.P.: Simulating physics with computers. Int. J. Theor. Phys. 21(6/7), 467–488 (1982)
Feynman, R., Leighton, R., Sands, M.: The Feynman Lectures on Physics. Addison Wesley, Boston (2005)
Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the 28th ACM Symposium on Theory of Computing, pp. 212–219 (1996)
Grover, L.K.: Quantum mechanics helps in searching for a needle in a haystack. Phys. Rev. Lett. 79(2), 325–328 (1997)
Grover, T.S., Wenk, S.L.: Relentless: From Good to Great to Unstoppable. Scribner, New York (2014)
Haven, E., Khrennikov, A.: Quantum Social Science. Cambridge University Press, Cambridge (2013)
Klir, G., Yuan, B.: Fuzzy Sets and Fuzzy Logic. Prentice Hall, Upper Saddle River (1995)
Kosheleva, O., Kreinovich, V.: How to introduce technical details of quantum computing in a theory of computation class: using the basic case of the Deutsch-Jozsa Algorithm. Int. J. Comput. Optim. 3(1), 83–91 (2016)
Kreinovich, V., Nguyen, H.T., Sriboonchitta, S.: Quantum ideas in economics beyond quantum econometrics. In: Anh, L., Dong, L., kreinovich, V., Thach, N. (eds.) Econometrics for Financial Applications, pp. 146–151. Springer, Cham (2018)
Lewis, M.: Moneyball: The Art of Winning an Unfair Game. W. W. Norton, New York (2004)
Mendel, J.M.: Uncertain Rule-Based Fuzzy Systems: Introduction and New Directions. Springer, Cham (2017)
Nguyen, H.T., Walker, E.A.: A First Course in Fuzzy Logic. Chapman and Hall/CRC, Boca Raton (2006)
Nielsen, M., Chuang, I.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)
Novák, V., Perfilieva, I., Močkoř, J.: Mathematical Principles of Fuzzy Logic. Kluwer, Boston (1999)
Schehtner, K.: Bridging the adoption gap for smart city technologies: an interview with Rob Kitchin. IEEE Pervas. Comput. 16(2), 72–75 (2017)
Shor, P.: Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. In: Proceedings of the 35th Annual Symposium on Foundations of Computer Science, Santa Fe, New Mexico, 20–22 November 1994 (1994)
Shor, P.: Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM J. Sci. Statist. Comput. 26, 1484–1509 (1997)
Thorne, K.S., Blandford, R.D.: Modern Classical Physics: Optics, Fluids, Plasmas, Elasticity, Relativity, and Statistical Physics. Princeton University Press, Princeton (2017)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Acknowledgments
This work was supported by the Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Thailand. We also acknowledge the partial support of the US National Science Foundation via grant HRD-1242122 (Cyber-ShARE Center of Excellence).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sriboonchitta, S., Nguyen, H.T., Kosheleva, O., Kreinovich, V., Nguyen, T.N. (2019). Quantum Approach Explains the Need for Expert Knowledge: On the Example of Econometrics. In: Kreinovich, V., Sriboonchitta, S. (eds) Structural Changes and their Econometric Modeling. TES 2019. Studies in Computational Intelligence, vol 808. Springer, Cham. https://doi.org/10.1007/978-3-030-04263-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-04263-9_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04262-2
Online ISBN: 978-3-030-04263-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)