JACIII Vol.24 No.5 pp. 599-603
doi: 10.20965/jaciii.2020.p0599


How to Combine (Dis)Utilities of Different Aspects into a Single (Dis)Utility Value, and How This Is Related to Geometric Images of Happiness

Laxman Bokati*1, Hoang Phuong Nguyen*2,†, Olga Kosheleva*3, and Vladik Kreinovich*1,*4

*1Computational Science Program, University of Texas at El Paso
500 W. University, El Paso, Texas 79968, USA

*2Division Informatics, Math-Informatics Faculty, Thang Long University
Nghiem Xuan Yem Road, Hoang Mai District, Hanoi, Vietnam

*3Department of Teacher Education, University of Texas at El Paso
500 W. University, El Paso, Texas 79968, USA

*4Department of Computer Science, University of Texas at El Paso
500 W. University, El Paso, Texas 79968, USA

Corresponding author

March 15, 2020
April 15, 2020
September 20, 2020
utility, disutility, decision making, geometric image of happiness

In many practical situations, a user needs our help in selecting the best out of a large number of alternatives. To be able to help, we need to understand the user’s preferences. In decision theory, preferences are described by numerical values known as utilities. It is often not feasible to ask the user to provide utilities of all possible alternatives, so we must be able to estimate these utilities based on utilities of different aspects of these alternatives. In this paper, we provide a general formula for combining utilities of aspects into a single utility value. The resulting formula turns out to be in good accordance with the known correspondence between geometric images and different degrees of happiness.

Cite this article as:
Laxman Bokati, Hoang Phuong Nguyen, Olga Kosheleva, and Vladik Kreinovich, “How to Combine (Dis)Utilities of Different Aspects into a Single (Dis)Utility Value, and How This Is Related to Geometric Images of Happiness,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.5, pp. 599-603, 2020.
Data files:
  1. [1] P. C. Fishburn, “Utility Theory for Decision Making,” John Wiley & Sons Inc., 1969.
  2. [2] V. Kreinovich, “Decision making under interval uncertainty (and beyond),” P. Guo and W. Pedrycz (Eds.), “Human-Centric Decision-Making Models for Social Sciences,” pp. 163-193, Springer-Verlag, 2014.
  3. [3] R. D. Luce and R. Raiffa, “Games and Decisions: Introduction and Critical Survey,” Dover, 1989.
  4. [4] H. T. Nguyen, O. Kosheleva, and V. Kreinovich, “Decision making beyond Arrow’s ‘impossibility theorem,’ with the analysis of effects of collusion and mutual attraction,” Int. J. of Intelligent Systems, Vol.24, No.1, pp. 27-47, 2009.
  5. [5] H. Raiffa, “Decision Analysis,” McGraw-Hill, 1997.
  6. [6] K. Autchariyapanitkul, O. Kosheleva, V. Kreinovich, and S. Sriboonchitta, “Quantum econometrics: how to explain its quantitative successes and how the resulting formulas are related to scale invariance, entropy, and fuzziness,” Proc. of the Int. Symp. on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM2018), pp. 264-275, 2018.
  7. [7] D. Kahneman, “Thinking, Fast and Slow,” Farrar, Straus, and Giroux, 2011.
  8. [8] J. Lorkowski and V. Kreinovich, “Granularity helps explain seemingly irrational features of human decision making,” W. Pedrycz and S.-M. Chen (Eds.), “Granular Computing and Decision-Making: Interactive and Iterative Approaches,” Springer-Verlag, pp. 1-31, 2015.
  9. [9] F. Y. Dong and K. Hirota, “Concept of fuzzy atmosfield and its visualization,” R. Seising, E. Trillas, C. Moraga, and S. Termini (Eds.), “On Fuzziness: A Homage to Lotfi A. Zadeh,” Volume 1, Springer, pp. 257-263, 2013.
  10. [10] J. A. Garcia-Sanchez, A. Shibata, K. Ohnishi, F. Dong, and K. Hirota, “Visualization method of emotion information for long distance interaction,” Proc. of the Joint 7th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (IEEE-HNICEM 2014) and the 6th Int. Conf on Computational Intelligence and Intelligent Informatics (ISCIII2014), Paper No. DSP-09, 2014.
  11. [11] K. Hirota, F. Dong, and J. A. Garcia Sanchez, “Multiagent smart communication based on CI technology,” V. Kreinovich and N. H. Phuong (Eds.), “Soft Computing for Biomedical Applications and Related Topics,” Springer-Verlag (in press).
  12. [12] Z.-T. Liu, M. Wu, D. Li, L.-F. Chen, F.-Y. Dong, Y. Yamazaki, and K. Hirota, “Concept of fuzzy atmosfield for representing communication atmosphere and its application to humans-robots interaction,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.1, pp. 3-17, 2013.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 16, 2022