Application of Artificial Intelligence Methods for Analysis of Material and Non-material Determinants of Functioning of Young Europeans in Times of Crisis in the Eurozone

Gawlik, Remigiusz (2014): Application of Artificial Intelligence Methods for Analysis of Material and Non-material Determinants of Functioning of Young Europeans in Times of Crisis in the Eurozone. Published in: Global and Regional Implications of the Euro Area Crisis No. E. Molendowski, P. Stanek (Eds.), Warszawa: PWN (2014): pp. 64-80.

Abstract

The study presents an analysis of possible applications of artificial intelligence methods for understanding, structuring and supporting the decision-making processes of European Youth in times of crisis in the Eurozone. Its main purpose is selecting a research method suitable for grasping and explaining the relations between social, economic and psychological premises when taking important life decisions by young Europeans at the beginning of their adult life. The interdisciplinary approach to science, assuming inclusion of economic phenomena in the analysis of issues belonging to other domains of science, contributes to further development of economics. Thus, the foundations of the economy are being redefined, whereas the dogma of rationality of consumer behaviour no longer binds. The researchers depart from their former deliberations on mass production , they also no longer claim that they understand the character of consumers’ preferences. The increased interest of economists in research instruments which employ artificial intelligence encourages them to use mathematical and IT tools to explain decision-making processes. This group of methods, based on fuzzy logic methodology offers the possibility of including into their research factors of a qualitative character (in addition to quantitative ones). It is equally important to identify possible fields of implementation of the results of qualitative – quantitative analysis in economic and social practice. A benefit for the business could be the possibility of better adjustments to new trends and consumers’ preferences. Social effects of the implementation should stem from supporting decision-making processes until facilitating professional and personal development of young people. Broadening of knowledge in this sphere will allow the responders to perform individual valuation of the material and non-material determinants of the quality of their life, which will eventually contribute to the growth of their life satisfaction. It will indirectly contribute to the increase of the overall level of satisfaction in the society.

• Item Type: MPRA Paper
• Language: English
• Keywords: Artificial Intelligence, determinants of decision-making, European Youth
• Subjects: C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; D - Microeconomics > D6 - Welfare Economics

References

Ćwiąkała - Małys, A., Nowak, W. Zarys metodologiczny analizy finansowej, Wydawnictwo Uniwersytetu Wrocławskiego, Wrocław 2005.

Dytczak, M., Wojtkiewicz, T.J., Metody wielokryterialnego wyboru systemów monitorowania, „Komputerowo zintegrowane zarządzanie”, Tom I, R. Knosala (red.), Oficyna Wydawnicza Polskiego Towarzystwa Zarządzania Produkcją, Opole 2010, s. 397 – 405.

Gawlik, R., Kopeć, M. (red.), Socio – Economic Implications of Global Economic Crises for European Youth. Summary Brochure. EU DG Education and Culture, Erasmus Lifelong Learning Programme, Intensive Programme 2012. 2012-1-PL-ERA10-29001. Uniwersytet Ekonomiczny w Krakowie, Kraków 2012.

Gawlik, R., The Influence of Intergovernmental Organizations on Main Determinants of the Open Systems Model with Correlation Analysis Method Application, “Organizations in Changing Environment. Current Problems, Concepts and Methods of Management”, W.M. Grudzewski, I. Hejduk, S. Trzcieliński (Eds.), IEA Press, Madison, WI 2007, s. 90 – 102.

Gawlik, R., Preliminary Criteria Reduction for the Application of Analytic Hierarchy Process Method, “Evolution and Revolution in the Global Economy: Enhancing Innovation and Competitiveness Worldwide”, Fuxman, L., Delener, N., Lu, F.V., Rivera – Solis, L.E. (Eds.). Global Business and Technology Association, New York, NY 2008, s. 366 – 374.

Gawlik, R., Strategic Decision-making in Times of Global Financial Crisis. “Business Strategies and Technological Innovations for Sustainable Development: Creating Global Prosperity for Humanity”, Delener, N., Fuxman, L., Lu, F.V., Putnova, A., Rivera-Solis, L.E. (Eds.). Global Business and Technology Association, New York, NY 2009, s. 431 – 439.

Gawlik, R., The Use of Analytic Hierarchy Process to Analyse International Corporations’ Operating Environment. „Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie: Ekonomia”, Nr 891, Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie, Kraków 2012, s. 19 30.

Gawlik, R., Zastosowanie metody Analitycznego Procesu Sieciowego do wspierania racjonalnych wyborów młodych Europejczyków. „Unia Europejska w 10 lat po największym rozszerzeniu”, Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu, Poznań 2014 [w druku].

Gawlik, R., Teczke, J., The Influence of Non - Governmental Organizations on Main Determinants of the Open Systems Model with Application of the Correlation Analysis Method. “Achieving Competitive Advantage Through Managing Global Resources”. Fuxman, L., Delener, N., Elifoglu, H., Wankel, Ch., Abel, I. (red.). New York: Global Business and Technology Association, New York, NY 2007, s. 728 – 735.

Góralski, P., Pietrzak, S., Ocena ryzyka kontynuacji działalności gospodarczej spółek ANR w aspekcie ich strategiczności oraz specyficznej funkcji rynkowej, „Zeszyty Naukowe Uniwersytetu Szczecińskiego: finanse, rynki finansowe, ubezpieczenia”, nr 47 (686), Wydawnictwo Naukowe Uniwersytetu Szczecińskiego, Szczecin 2011, s. 59 – 69.

Hopfield, J.J., Neural networks and physical systems with emergent collective computational abilities, “Proceedings of National Academy of Sciences of United States of America: Biophysics, Vol. 79, 1982, s. 2554 – 2558.

Jajuga, K., Sokołowski, A., Bock, H.-H. (red.), Classification, Clustering and Data Analysis. Recent Advances and Applications, Springer Verlag, Berlin – Heidelberg 2002.

Krupa, T., Ostrowska, T., Model procesu decyzyjnego w systemie hierarchicznym, „Komputerowo Zintegrowane Zarządzanie”, Tom II, R. Knosala (red.), WNT, Warszawa 2001, s. 5 14.

Lin, C.L. and Tzeng, G.H., A value-created system of science (technology) park by using DEMETEL. “Expert Systems with Applications”, No 36, 2009, s. 9683 – 9697.

Luger, G., Stubblefield, W., Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 5th Ed., The Benjamin / Cummings Publishing Company, Inc, San Francisco, CA 2004.

McCarthy, J., Artificial Intelligence. Logic and Formalizing Common Sense, “Philosophical Logic and Artificial Intelligence”, Thomason, R.H. (ed.), Klüver Academic, Norwell, MA 1989.

Motyka, S., Ocena skuteczności wprowadzania innowacji technicznych w przedsiębiorstwach przemysłu maszynowego, praca doktorska, promotor prof. zw. dr hab. inż. J. Gawlik, Politechnika Krakowska, Kraków 2007.

Neapolitan, R., Jiang, X., Contemporary Artificial Intelligence, Chapman & Hall / CRC, London 2012. Nilsson, N.J., The Quest for Artificial Intelligence: A History of Ideas and Achievements, Cambridge University Press, Cambridge 2009.

Ogryczak, W., Multicriteria Models for Fair Resource Allocation, “Control and Cybernetics”, Vol. 36, 2007, s. 303 – 332.

Ostrowska, T. Modelowanie sieciowych procesów decyzyjnych techniką AIDA, „Komputerowo zintegrowane zarządzanie”, Tom II, R. Knosala (red.), Oficyna Wydawnicza Polskiego Towarzystwa Zarządzania Produkcją, Opole 2007, s. 168 176.

Poole, D., Mackworth, A., Goebel, R., Computational Intelligence: A Logical Approach. Oxford University Press, New York, NY 1998.

Russell, I., Neural Networks in the Undergraduate Curriculum. “Collegiate Microcomputer”, Vol. 9 (1), 1991, s. 92 – 97.

Russell, S. J., Norvig, P., Artificial Intelligence: A Modern Approach, 2nd Ed., Prentice Hall, Upper Saddle River, NJ 2003.

Saaty, T.L., The Analytic Hierarchy Process, RWS Publications, Pittsburgh, PA 1996.

Saaty, T.L., Decision-making for Leaders: The Analytic Hierarchy Process for Decisions in a Complex World. 3rd Ed., RWS Publications, Pittsburgh, PA 2000.

Saaty, T.L. The Analytic Network Process: Decision-making With Dependence and Feedback, 2nd Ed., RWS Publications, Pittsburgh, PA 2001.

Tadeusiewicz, R. Wstęp do sieci neuronowych., „Biocybernetyka i inżynieria biomedyczna 2000”, Nałęcz, M. (red.), Tom 6: Sieci Neuronowe, W. Duch, J. Korbicz, L. Rutkowski, R. Tadeusiewicz (red. tomu), Akademicka Oficyna Wydawnicza Exit, Warszawa 2000, s. 3 – 28.

Winston, P.H., Artificial Intelligence, Addison – Wesley, Reading, MA 1984.

Yang, Y.P., Shieh, H.M., Leu, J.D. and Tzeng, G.H., A novel hybrid MCDM model combined with DEMATEL and ANP with applications., “International Journal of Operational Research”, Vol. 5 (3), 2008, s. 160 – 168.

Zoffer, J., Bahurmoz, A., Hamid, M.K., Minutolo, M., Saaty, T.L., Synthesis of Complex Criteria Decision-making: A Case Towards a Consensus Agreement for a Middle East Conflict Resolution., “Group Decision and Negotiation”, Vol. 17, No. 5, 2008, s. 363 – 385.