Share:


A decision-making framework based on the prospect theory under an intuitionistic fuzzy environment

    Jing Gu Affiliation
    ; Zijian Wang Affiliation
    ; Zeshui Xu Affiliation
    ; Xuezheng Chen Affiliation

Abstract

Uncertainty and ambiguity are frequently involved in the decision-making process in our daily life. This paper develops a generalized decision-making framework based on the prospect theory under an intuitionistic fuzzy environment, by closely integrating the prospect theory and the intuitionistic fuzzy sets into our framework. We demonstrate how to compute the intuitionistic fuzzy prospect values as the reference values for decision-making and elaborate a four-step editing phase and a valuation phase with two key functions: the value function and the weighting function. We then conduct experiments to test our decision- making methodology and the key features of our framework. The experimental results show that the shapes of the value function and the weighting function in our framework are in line with those of prospect theory. The methodology proposed in this paper to elicit prospects that are not only under uncertainty but also under ambiguity. We reveal the decision-making behavior pattern through comparing the parameters. People are less risk averse when making decisions under an intuitionistic fuzzy environment than under uncertainty. People still underestimate the probability of the events in our experiment. Further, the choices of participants in the experiments are consistent with the addition and multiplication principles of our framework.

Keyword : decision processes, prospect theory, intuitionistic fuzzy set, intuitionistic fuzzy prospect value

How to Cite
Gu, J., Wang, Z., Xu, Z., & Chen, X. (2018). A decision-making framework based on the prospect theory under an intuitionistic fuzzy environment. Technological and Economic Development of Economy, 24(6), 2374-2396. https://doi.org/10.3846/tede.2018.6981
Published in Issue
Dec 21, 2018
Abstract Views
1321
PDF Downloads
838
Creative Commons License

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

References

Allais, M. (1953). Le comportement de i᾽homme rationnel devant le risque: critique des postulats et axiomes de i᾽cole americaine. Econometrica, 21(4), 503-546. https://doi.org/10.2307/1907921

Andrade, R. A. E., González, E., Fernández, E., & Gutiérrez, S. M. (2014). A fuzzy approach to prospect theory. In R. Espin, R. Pérez, A. Cobo, J. Marx, & A. Valdés. (Eds.), Soft computing for business intelligence. Studies in computational intelligence (Vol. 537). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-53737-0_3

Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96. https://doi.org/10.1016/S0165-0114(86)80034-3

Atanassov, K. T., & Gargov, G. (1989). Interval valued intuitionistic fuzzy sets. Fuzzy Sets and Systems, 31(3), 343-349. https://doi.org/10.1016/0165-0114(89)90205-4

Chen, S. M., & Tan, J. M. (1994). Handling multicriteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets & Systems, 67(2), 163-172. https://doi.org/10.1016/0165-0114(94)90084-1

Edwards, K. D. (1996). Prospect theory: a literature review. International Review of Financial Analysis, 5(1), 19-38. https://doi.org/10.1016/S1057-5219(96)90004-6

Gao, J., & Liu, H. (2016). A new prospect projection multi-criteria decision-making method for interval-valued intuitionistic fuzzy numbers. Information, 7(4), 64.

Gonzalez, R., & Wu, G. (1999). On the shape of the probability weighting function. Cognitive Psychology, 38(1), 129-166. https://doi.org/10.1006/cogp.1998.0710

Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47(2), 140-170. https://doi.org/10.2307/1914185

Krohling, R. A., & Souza, T. T. M. D. (2012). Combining prospect theory and fuzzy numbers to multicriteria decision-making. Expert Systems with Applications, 39(13), 11487-11493. https://doi.org/10.1016/j.eswa.2012.04.006

Leclerc, F., Schmitt, B. H., & Dubé, L. (1995). Waiting time and decision-making: is time like money? Journal of Consumer Research, 22(1), 110-119. https://doi.org/10.1086/209439

Levy, M., & Levy, H. (2002). Prospect theory: much ado about nothing? Management Science, 48(10), 1334-1349. https://doi.org/10.1287/mnsc.48.10.1334.276

Li, P., Liu, S. F., & Zhu, J. J. (2012). Intuitionistic fuzzy stochastic multi-criteria decision-making methods based on prospect theory. Control and Decision, 27(11), 1601-1606.

Li, P., Yang, Y., & Wei, C. (2017). An intuitionistic fuzzy stochastic decision-making method based on case-based reasoning and prospect theory. Mathematical Problems in Engineering, 6, 1-13. https://doi.org/10.1155/2017/2874954

Li, X. H. (2013). Intuitionistic trapezoidal fuzzy multi-attribute decision-making method based on cumulative prospect theory and Choquet integral. Application Research of Computers, 30(8), 2422-2425.

Li, X. H., & Chen, X. H. (2014). Extension of the TOPSIS method based on prospect theory and trapezoidal intuitionistic fuzzy numbers for group decision-making. Journal of Systems Engineering and Electronics, 23(2), 231-247. https://doi.org/10.1007/s11518-014-5244-y

Liu, H. W., & Wang, G. J. (2007). Multi-criteria decision-making methods based on intuitionistic fuzzy sets. European Journal of Operational Research, 179(1), 220-233. https://doi.org/10.1016/j.ejor.2006.04.009

Liu, P., Jin, F., Zhang, X., Su, Y., & Wang, M. (2011). Research on the multi-attribute decision-making under risk with interval probability based on prospect theory and the uncertain linguistic variables. Knowledge-Based Systems, 24(4), 554-561. https://doi.org/10.1016/j.knosys.2011.01.010

Mayer, P. C. (1995). Electricity conservation: consumer rationality versus prospect theory. Contemporary Economic Policy, 13(2), 109-118. https://doi.org/10.1111/j.1465-7287.1995.tb00747.x

Meng, F., Tan, C., & Chen, X. (2015). An approach to Atanassov᾽ interval-valued intuitionistic fuzzy multi-attribute decision making based on prospect theory. International Journal of Computational Intelligence Systems, 8(3), 591-605. https://doi.org/10.1080/18756891.2015.1036224

Newman, D. P. (1980). Prospect theory: implications for information evaluation. Accounting Organizations and Society, 5(2), 217-230. https://doi.org/10.1016/0361-3682(80)90011-2

Payne, J. W., Laughhunn, D. J., & Crum, R. (1984). Multiattribute risky choice behavior: the editing of complex prospects. Management Science, 30(11), 1350-1361. https://doi.org/10.1287/mnsc.30.11.1350

Rieger, M. O., Wang, M., & Hens, T. (2015). Risk preferences around the world. Management Science, 61(3), 637-648.

Sebora, T. C., & Cornwall, J. R. (1995). Expected utility theory vs prospect theory: implications for strategic decision makers. Journal of Managerial Issues, 7(1), 41-61.

Tversky, A., & Fox, C. R. (1995). Weighing risk and uncertainty. Psychological Review, 102(2), 269-283. https://doi.org/10.1037/0033-295X.102.2.269

Tversky, A., & Kahneman, D. (1986). Rational choice and the framing of decisions. Journal of Business, 59(4), 251-278.

Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297-323. https://doi.org/10.1086/296365

Von Neumann, J. L., & Morgenstern, O. V. (1953). The theory of games and economic behavior. Princeton: University Press.

Wang, J. Q., & Zhang, Z. (2009). Aggregation operators on intuitionistic trapezoidal fuzzy number and its application to multi-criteria decision-making problems. Journal of Systems Engineering and Electronics, 20(2), 321-326.

Wang, J. Q., & Nie, R. R. (2012). Multi-criteria group decision-making method based on intuitionistic trapezoidal fuzzy information. System Engineering Theory and Practice, 32(8), 1747-1753.

Wu, G., & Gonzalez, R. (1999). Nonlinear decision weights in choice under uncertainty. Management Science, 45(1), 74-85. https://doi.org/10.1287/mnsc.45.1.74

Xu, Z. S., & Xia, M. M. (2011). Induced generalized intuitionistic fuzzy operators. Knowledge-Based Systems, 24(2), 197-209. https://doi.org/10.1016/j.knosys.2010.04.010

Xu, Z. S. (2007). Intuitionistic fuzzy aggregation operators. Information Fusion, 14(1), 108-116.

Xu, Z. S., & Cai, X. Q. (2012). Intuitionistic fuzzy information aggregation: theory and applications. Berlin: Springer-Verlag.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X