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SÜREKLİ ARTAN BİR FAYDA FONKSİYONUNUN MODELLENMESİ

BUILDING AN INCREASING CONTINUOUS UTILITY FUNCTION

Journal Name:

  • Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi

Publication Year:

  • 2008

Key Words:

Keywords (Original Language):

Author NameUniversity of Author
Abstract (2. Language): 
The paper focuses on modern analytical techniques for construction of utility functions over prizes, where the preferences of the decision maker are strictly increasing. If chosen and applied properly these methods facilitate the analysis and guarantee precise description of the decision makers’ preferences. The paper discusses modern analytical techniques, such as a modified arctg(.) form of the utility function, which contains prior information for the most typical risk attitude over lotteries, whose prizes can be both profits and losses. Also discussed is a power approximation of the utility.
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Abstract (Original Language): 
Bu çalışma karar oluşturucuların özellikleri kuvvetli bir şekilde artarken ödüller için kurulmuş fayda fonksiyonunun inşası için modern analitik teknikler üzerine odaklanmaktadır. Eğer bu metotlar seçilir ve uygun bir şekilde uygulanırsa analizi kolaylaştırırlar ve karar oluşturucularının özelliklerini tam anlamıyla garanti ederler. Bu çalışma ödülü hem kar hem zarar olabilen piyangolar üzerine en tipik risk davranışı için ön bilgi içeren fayda fonksiyonunun bir modifiye edilmiş arctg(.) biçimi gibi modern analitik teknikleri tartışmaktadır. Ayrıca fayda yaklaşımı da tartışılmaktadır.
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REFERENCES

References: 

1.
Ramsay, F. P. (1931). Truth and probability. The logical foundations of mathematics and other essays. Kegan Paul. (Reprinted: Kyburg, H.E. Jr., Smolker, H.E. (1964) Studies in subjective probability (eds), 61-92, John Wiley).
2.
Von Neumann, J., O. Morgenstern (1947). Theory of games and economic behavior. Second Edition. Princeton University Press.
3.
French, S. (1993). Decision theory: an introduction to the mathematics of rationality. Ellis Horwood.
4.
Tenekedjiev, K., Nikolova, N.D. (2007). Decision theory – subjectivity, reality and fuzzy rationality, CIELA, Sofia (in Bulgarian).
5.
Fishburn, P. C. (1982). The foundations of expected utility. D. Reidel.
6.
Tenekedjiev, K., Nikolova, N.D., Dimitrakiev, D. (2004) Analytical Utility Function оver One-Dimensional Continuous Set with Increasing Preferences, Proc. Fifth International Scientific-Applied Conference on Contemporary Problems in Company Management Theory and Practice, pp. 246-251, Varna, Bulgaria, 2004
7.
Tenekedjiev, K. (2007). Triple bisection algorithms for 1-D utility elicitation in the case of monotonic preferences. Advanced Studies in Contemporary Mathematics, 14(2), 259-281
8.
Press, W. H., Teukolski, S. A., Vetterling, W. T., B. P. Flannery (1992). Numerical recipes – the art of scientific computing. Cambridge University Press.
9.
McCord, M., De Neufville, R. (1986). Lottery equivalents’: reduction of the certainty effect problem in utility assessment. – Management Science, 32, 56–60.
10.
Tenekedjiev, K., Nikolova, N.D., Dimitrakiev, D. (2002). Theory and practice of risk decisions, MARC Publications, Varna (in Bulgarian).
11.
Tenekedjiev, K., Nikolova, N.D., Pfliegl, R. (2006). Utility elicitation with the uncertain equivalence method. Comptes Rendus De L’Academie Bulgare des Sciences, 59(3) 283-288.
12.
Pratt, J. W. (1964). Risk aversion in the small and in the large. – Econometrica, 32, 122-136.
13.
Clemen, R. (1996). Making hard decisions: an introduction to decision analysis. Second Edition. Duxbury Press, Wadsworth Publishing Company.
14.
Keeney, R. L., H. Raiffa (1993). Decisions with multiple objectives: preference and value tradeoffs. Cambridge University Press.
15.
Swalm, R. (1966). Utility theory – insight into risk taking. – Harvard Business Review, 44, 123–136
16.
McNamee, P., Celona, J. (1987). Decision analysis for the professional with super tree. Redwood City, CA: Scientific Press.
17.
Tenekedjiev, K., Nikolova, N.D., Dimitrakiev, D. (2007) Analytical One-Dimensional Utility – Comparison of Power approximation and Arctg-approximation, Engineering Sciences, 4, 20-34, 2007 (in Bulgarian)
18.
Nikolova, N.D. (2007). Empirical connection between the number of elicited nodes and the quality of the analytic approximation of a one dimensional utility function, Mechanical engineering and mechanical knowledge, series ‘Economics and Management’ 2, 99-110 (in Bulgarian).
19.
Schlaifer, R.O.(1971). Computer programs for elementary decision analysis. Division of Research, Graduate School of Business Administration, Harvard University, Cambridge, Mass.

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