An enhancement EDAS method based on Prospect Theory
Abstract
Decision-making is the process of carefully considering multiple options and choosing the best one. The EDAS (evaluation based on distance from average solution) method has been studied in many multi-attributes decision-making (MADM) problem which assumes decisionmaking under absolute rationality. However, people usually show the characteristics of bounded rationality in the real decision-making process. Prospect theory (PT) utilizes gains and losses relative to the reference point to explain this phenomenon better. In this paper, an enhancement EDAS method based on PT will be proposed, which shows better properties in practice. We apply the traditional EDAS method and enhancement EDAS method to the same case and we utilize the sensitivity analysis and comparative analysis to analyze their performances. The result shows that our approach has a superiority compared with the traditional EDAS method. The methods we present are of great significance for investment decision-making problems, new product development, design plan selection and supplier selection.
Keyword : decision-making, bounded rationality, EDAS, PT, sensitivity analysis
How to Cite
Huang, Y., Lin, R., & Chen, X. (2021). An enhancement EDAS method based on Prospect Theory. Technological and Economic Development of Economy, 27(5), 1019-1038. https://doi.org/10.3846/tede.2021.15038
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Evans, J. R. (1984). Sensitivity analysis in decision theory. Decision Sciences, 15(2), 239–247. https://doi.org/10.1111/j.1540-5915.1984.tb01211.x
Fallahpour, A., Olugu, E. U., & Musa, S. N. (2017). A hybrid model for supplier selection: Integration of AHP and multi expression programming (MEP). Neural Computing & Applications, 28, 499–504. https://doi.org/10.1007/s00521-015-2078-6
Fishburn, P. C., Murphy, A. H., & Isaacs, H. H. (1968). Sensitivity of decisions to probability estimation errors: A reexamination. Operations Research, 16(2), 254–267. https://doi.org/10.1287/opre.16.2.254
Gomes, L., & Lima, M. (1992). TODIM: Basics and application to multicriteria ranking of projects with environmental impacts. Foundations of Computing and Decision Sciences, 16, 113–127.
Gomes, L., & Rangel, L. A. D. (2009). An application of the TODIM method to the multicriteria rental evaluation of residential properties. European Journal of Operational Research, 193(1), 204–211. https://doi.org/10.1016/j.ejor.2007.10.046
Hashiyama, T., Furuhashi, T., & Uchikawa, Y. (1993). A study on a multi-attribute decision making process using fuzzy neural network. In Proceedings of the Korean Institute of Intelligent Systems Conference (pp. 810–813). Korean Institute of Intelligent Systems.
He, T., Wei, G., Lu, J., Wu, J., Wei, C., & Guo, Y. (2020). A novel EDAS based method for multiple attribute group decision making with pythagorean 2-tuple linguistic information. Technological and Economic Development of Economy, 26(6), 1125–1138. https://doi.org/10.3846/tede.2020.12733
He, Y., Lei, F., Wei, G. W., Wang, R., Wu, J., & Wei, C. (2019). EDAS method for multiple attribute group decision making with probabilistic uncertain linguistic information and its application to green supplier selection. International Journal of Computational Intelligence Systems, 12(2), 1361–1370. https://doi.org/10.2991/ijcis.d.191028.001
Hillier, F. S., Lieberman, G. J., Nag, B., & Basu, P. (2012). Introduction to operations research. Tata McGraw-Hill Education. https://www.iimcal.ac.in/sites/all/files/pdfs/tmh-book-cover.pdf
Keshavarz Ghorabaee, M., Zavadskas, E. K., Amiri, M., & Turskis, Z. (2016). Extended EDAS method for fuzzy multi-criteria decision-making: An application to supplier selection. International Journal of Computers Communications & Control, 11(3), 358–371. https://doi.org/10.15837/ijccc.2016.3.2557
Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435–451. https://doi.org/10.15388/Informatica.2015.57
Lahdelma, R., & Salminen, P. (2009). Prospect theory and stochastic multicriteria acceptability analysis (SMAA). Omega, 37(5), 961–971. https://doi.org/10.1016/j.omega.2008.09.001
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
Liu, P., & Zhang, X. (2010). The study on multi-attribute decision-making with risk based on linguistic variable. International Journal of Computational Intelligence Systems, 3(5), 601–609. https://doi.org/10.2991/ijcis.2010.3.5.9
Malik, N., & Shabir, M. (2019). Rough fuzzy bipolar soft sets and application in decision-making problems. Soft Computing, 23, 1603–1614. https://doi.org/10.1007/s00500-017-2883-1
Masuda, T. (1990). Hierarchical sensitivity analysis of priority used in analytic hierarchy process. International Journal of Systems Science, 21(2), 415–427. https://doi.org/10.1080/00207729008910371
Morgenstern, O., & Von Neumann, J. (1953). Theory of games and economic behavior. Princeton University Press.
Myers, J. H., & Alpert, M. I. (1968). Determinant buying attitudes: meaning and measurement. Journal of Marketing, 32(4), 13–20. https://doi.org/10.1177/002224296803200404
Pawlak, Z., & Sowinski, R. (1994). Rough set approach to multi-attribute decision analysis. European Journal of Operational Research, 72(3), 443–459. https://doi.org/10.1016/0377-2217(94)90415-4
Ren, Z. L., Xu, Z. S., & Wang, H. (2019). The strategy selection problem on artificial intelligence with an integrated VIKOR and AHP method under probabilistic dual hesitant fuzzy information. IEEE Access, 7, 103979–103999. https://doi.org/10.1109/ACCESS.2019.2931405
Rostamzadeh, R., Esmaeili, A., Nia, A. S., Saparauskas, J., & Keshavarz Ghorabaee, M. (2017). A fuzzy ARAS method for supply chain management performance measurement in SMEs under uncertainty. Transformations in Business & Economics, 16, 319–348. https://www.researchgate.net/publication/321797571_A_Fuzzy_ARAS_Method_for_Supply_Chain_Management_Performance_Measurement_in_SMEs_under_Uncertainty
Roy, J., Sharma, H. K., Kar, S., Zavadskas, E. K., & Saparauskas, J. (2019). An extended COPRAS model for multi-criteria decision-making problems and its application in web-based hotel evaluation and selection. Economic Research-Ekonomska Istrazivanja, 32(1), 219–253. https://doi.org/10.1080/1331677X.2018.1543054
Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234–281. https://doi.org/10.1016/0022-2496(77)90033-5
Salminen, P. (1994). Solving the discrete multiple criteria problem using linear prospect theory. European Journal of Operational Research, 72(1), 146–154. https://doi.org/10.1016/S0377-2217(12)80001-4
Schmidt, U., Starmer, C., & Sugden, R. (2008). Third-generation prospect theory. Journal of Risk and Uncertainty, 36, 203. https://doi.org/10.1007/s11166-008-9040-2
Schmidt, U., & Zank, H. (2009). A simple model of cumulative prospect theory. Journal of Mathematical Economics, 45(3–4), 308–319. https://doi.org/10.1016/j.jmateco.2008.12.001
Starr, M. K. (1966). A discussion of some normative criteria for decision-making under uncertainty. IMR; Industrial Management Review (pre-1986), 8, 71.
Tabatabaei, M. H., Amiri, M., Firouzabadi, S., Ghahremanloo, M., Keshavarz-Ghorabaee, M., & Saparauskas, J. (2019). A new group decision-making model based on BWM and its application to managerial problems. Transformations in Business & Economics, 18(2), 197–214. https://www.researchgate.net/publication/333666237_A_New_Group_Decision-Making_Model_based_on_BWM_and_its_Application_to_Managerial_Problems
Tian, X., Xu, Z., Gu, J., & Herrera-Viedma, E. (2018). How to select a promising enterprise for venture capitalists with prospect theory under intuitionistic fuzzy circumstance? Applied Soft Computing, 67, 756–763. https://doi.org/10.1016/j.asoc.2017.04.027
Tian, X. L., Xu, Z. S., & Gu, J. (2019). An extended TODIM based on cumulative prospect theory and its application in venture capital. Informatica, 30(2), 413–429. https://doi.org/10.15388/Informatica.2019.212
Tversky, A., & Kahneman, D. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323. https://doi.org/10.1007/BF00122574
Wakker, K. (2007). Astrodynamics I. Lecture Notes AE4-874, Part, 1. Delft University of Technology, Faculty of Aerospace Engineering. The Netherlands.
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. https://www.researchgate.net/publication/260650574_Aggregation_operators_on_intuitionistic_trapezoidal_fuzzy_number_and_its_application_to_multi-criteria_decision_making_problems
Wang, L., Wang, Y.-M., & Martínez, L. (2017). A group decision method based on prospect theory for emergency situations. Information Sciences, 418, 119–135. https://doi.org/10.1016/j.ins.2017.07.037
Wang, S., Wei, G., Wu, J., Wei, C., & Guo, Y. (2021). Model for selection of hospital constructions with probabilistic linguistic GRP method. Journal of Intelligent & Fuzzy Systems, 40(1), 1245–1259. https://doi.org/10.3233/JIFS-201543
Wei, C., Wu, J., Guo, Y., & Wei, G. (2021a). Green supplier selection based on CODAS method in probabilistic uncertain linguistic environment. Technological and Economic Development of Economy, 27(3), 530–549. https://doi.org/10.3846/tede.2021.14078
Wei, G., Wu, J., Guo, Y., Wang, J., & Wei, C. (2021b). An extended COPRAS model for multiple attribute group decision making based on single-valued neutrosophic 2-tuple linguistic environment. Technological and Economic Development of Economy, 27(2), 353–368. https://doi.org/10.3846/tede.2021.14057
Yoon, K., & Hwang, C. L. (1981). TOPSIS (technique for order preference by similarity to ideal solution) – a multiple attribute decision making. In Multiple attribute decision making: Methods and applications, a state-of-the-at survey. Springer Verlag.
Zhang, Y., Wei, G., Guo, Y., & Wei, C. (2021). TODIM method based on cumulative prospect theory for multiple attribute group decision‐making under 2‐tuple linguistic Pythagorean fuzzy environment. International Journal of Intelligent Systems, 36(6), 2548–2571. https://doi.org/10.1002/int.22393
Zhao, M., Wei, G., Wei, C., & Guo, Y. (2021a). CPT‐TODIM method for bipolar fuzzy multi‐attribute group decision making and its application to network security service provider selection. International Journal of Intelligent Systems, 36(5), 1943–1969. https://doi.org/10.1002/int.22367
Zhao, M., Wei, G., Wei, C., Wu, J., & Wei, Y. (2021b). Extended CPT-TODIM method for intervalvalued intuitionistic fuzzy MAGDM and its application to urban ecological risk assessment. Journal of Intelligent & Fuzzy Systems, 40(3), 4091–4106. https://doi.org/10.3233/JIFS-200534
Zhao, M., Wei, G., Wu, J., Guo, Y., & Wei, C. (2021c). TODIM method for multiple attribute group decision making based on cumulative prospect theory with 2‐tuple linguistic neutrosophic sets. International Journal of Intelligent Systems, 36(3), 1199–1222. https://doi.org/10.1002/int.22338
Zhao, M. W., Wei, G. W., Wei, C., & Wu, J. (2021d). Pythagorean fuzzy TODIM method based on the cumulative prospect theory for MAGDM and its application on risk assessment of science and technology projects. International Journal of Fuzzy Systems. https://doi.org/10.1007/s40815-020-00986-8
Bagherzadeh, A., & Gholizadeh, A. (2017). Parametric-based neural networks and TOPSIS modeling in land suitability evaluation for alfalfa production using GIS. Modeling Earth Systems and Environment, 3, 2. https://doi.org/10.1007/s40808-016-0263-y
Barnard, C., & Simon, H. A. (1947). Administrative behavior. A study of decision-making processes in administrative organization. Free Press. https://www.academia.edu/34589107/ADMINISTRATIVE_BEHAVIOR_A_Study_of_Decision_Making_Processes_in_Administrative_Organization
Cheng, S. L., & Chang, H. C. (2006). Multicriteria automatic essay assessor generation by using TOPSIS model and genetic algorithm. In M. Ikeda, K. D. Ashley, & T. W. Chan (Eds.), Lecture notes in computer science: Vol. 4053. Intelligent tutoring systems, proceedings (pp. 11–20). Springer. https://doi.org/10.1007/11774303_2
Evans, J. R. (1984). Sensitivity analysis in decision theory. Decision Sciences, 15(2), 239–247. https://doi.org/10.1111/j.1540-5915.1984.tb01211.x
Fallahpour, A., Olugu, E. U., & Musa, S. N. (2017). A hybrid model for supplier selection: Integration of AHP and multi expression programming (MEP). Neural Computing & Applications, 28, 499–504. https://doi.org/10.1007/s00521-015-2078-6
Fishburn, P. C., Murphy, A. H., & Isaacs, H. H. (1968). Sensitivity of decisions to probability estimation errors: A reexamination. Operations Research, 16(2), 254–267. https://doi.org/10.1287/opre.16.2.254
Gomes, L., & Lima, M. (1992). TODIM: Basics and application to multicriteria ranking of projects with environmental impacts. Foundations of Computing and Decision Sciences, 16, 113–127.
Gomes, L., & Rangel, L. A. D. (2009). An application of the TODIM method to the multicriteria rental evaluation of residential properties. European Journal of Operational Research, 193(1), 204–211. https://doi.org/10.1016/j.ejor.2007.10.046
Hashiyama, T., Furuhashi, T., & Uchikawa, Y. (1993). A study on a multi-attribute decision making process using fuzzy neural network. In Proceedings of the Korean Institute of Intelligent Systems Conference (pp. 810–813). Korean Institute of Intelligent Systems.
He, T., Wei, G., Lu, J., Wu, J., Wei, C., & Guo, Y. (2020). A novel EDAS based method for multiple attribute group decision making with pythagorean 2-tuple linguistic information. Technological and Economic Development of Economy, 26(6), 1125–1138. https://doi.org/10.3846/tede.2020.12733
He, Y., Lei, F., Wei, G. W., Wang, R., Wu, J., & Wei, C. (2019). EDAS method for multiple attribute group decision making with probabilistic uncertain linguistic information and its application to green supplier selection. International Journal of Computational Intelligence Systems, 12(2), 1361–1370. https://doi.org/10.2991/ijcis.d.191028.001
Hillier, F. S., Lieberman, G. J., Nag, B., & Basu, P. (2012). Introduction to operations research. Tata McGraw-Hill Education. https://www.iimcal.ac.in/sites/all/files/pdfs/tmh-book-cover.pdf
Keshavarz Ghorabaee, M., Zavadskas, E. K., Amiri, M., & Turskis, Z. (2016). Extended EDAS method for fuzzy multi-criteria decision-making: An application to supplier selection. International Journal of Computers Communications & Control, 11(3), 358–371. https://doi.org/10.15837/ijccc.2016.3.2557
Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26(3), 435–451. https://doi.org/10.15388/Informatica.2015.57
Lahdelma, R., & Salminen, P. (2009). Prospect theory and stochastic multicriteria acceptability analysis (SMAA). Omega, 37(5), 961–971. https://doi.org/10.1016/j.omega.2008.09.001
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
Liu, P., & Zhang, X. (2010). The study on multi-attribute decision-making with risk based on linguistic variable. International Journal of Computational Intelligence Systems, 3(5), 601–609. https://doi.org/10.2991/ijcis.2010.3.5.9
Malik, N., & Shabir, M. (2019). Rough fuzzy bipolar soft sets and application in decision-making problems. Soft Computing, 23, 1603–1614. https://doi.org/10.1007/s00500-017-2883-1
Masuda, T. (1990). Hierarchical sensitivity analysis of priority used in analytic hierarchy process. International Journal of Systems Science, 21(2), 415–427. https://doi.org/10.1080/00207729008910371
Morgenstern, O., & Von Neumann, J. (1953). Theory of games and economic behavior. Princeton University Press.
Myers, J. H., & Alpert, M. I. (1968). Determinant buying attitudes: meaning and measurement. Journal of Marketing, 32(4), 13–20. https://doi.org/10.1177/002224296803200404
Pawlak, Z., & Sowinski, R. (1994). Rough set approach to multi-attribute decision analysis. European Journal of Operational Research, 72(3), 443–459. https://doi.org/10.1016/0377-2217(94)90415-4
Ren, Z. L., Xu, Z. S., & Wang, H. (2019). The strategy selection problem on artificial intelligence with an integrated VIKOR and AHP method under probabilistic dual hesitant fuzzy information. IEEE Access, 7, 103979–103999. https://doi.org/10.1109/ACCESS.2019.2931405
Rostamzadeh, R., Esmaeili, A., Nia, A. S., Saparauskas, J., & Keshavarz Ghorabaee, M. (2017). A fuzzy ARAS method for supply chain management performance measurement in SMEs under uncertainty. Transformations in Business & Economics, 16, 319–348. https://www.researchgate.net/publication/321797571_A_Fuzzy_ARAS_Method_for_Supply_Chain_Management_Performance_Measurement_in_SMEs_under_Uncertainty
Roy, J., Sharma, H. K., Kar, S., Zavadskas, E. K., & Saparauskas, J. (2019). An extended COPRAS model for multi-criteria decision-making problems and its application in web-based hotel evaluation and selection. Economic Research-Ekonomska Istrazivanja, 32(1), 219–253. https://doi.org/10.1080/1331677X.2018.1543054
Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234–281. https://doi.org/10.1016/0022-2496(77)90033-5
Salminen, P. (1994). Solving the discrete multiple criteria problem using linear prospect theory. European Journal of Operational Research, 72(1), 146–154. https://doi.org/10.1016/S0377-2217(12)80001-4
Schmidt, U., Starmer, C., & Sugden, R. (2008). Third-generation prospect theory. Journal of Risk and Uncertainty, 36, 203. https://doi.org/10.1007/s11166-008-9040-2
Schmidt, U., & Zank, H. (2009). A simple model of cumulative prospect theory. Journal of Mathematical Economics, 45(3–4), 308–319. https://doi.org/10.1016/j.jmateco.2008.12.001
Starr, M. K. (1966). A discussion of some normative criteria for decision-making under uncertainty. IMR; Industrial Management Review (pre-1986), 8, 71.
Tabatabaei, M. H., Amiri, M., Firouzabadi, S., Ghahremanloo, M., Keshavarz-Ghorabaee, M., & Saparauskas, J. (2019). A new group decision-making model based on BWM and its application to managerial problems. Transformations in Business & Economics, 18(2), 197–214. https://www.researchgate.net/publication/333666237_A_New_Group_Decision-Making_Model_based_on_BWM_and_its_Application_to_Managerial_Problems
Tian, X., Xu, Z., Gu, J., & Herrera-Viedma, E. (2018). How to select a promising enterprise for venture capitalists with prospect theory under intuitionistic fuzzy circumstance? Applied Soft Computing, 67, 756–763. https://doi.org/10.1016/j.asoc.2017.04.027
Tian, X. L., Xu, Z. S., & Gu, J. (2019). An extended TODIM based on cumulative prospect theory and its application in venture capital. Informatica, 30(2), 413–429. https://doi.org/10.15388/Informatica.2019.212
Tversky, A., & Kahneman, D. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323. https://doi.org/10.1007/BF00122574
Wakker, K. (2007). Astrodynamics I. Lecture Notes AE4-874, Part, 1. Delft University of Technology, Faculty of Aerospace Engineering. The Netherlands.
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. https://www.researchgate.net/publication/260650574_Aggregation_operators_on_intuitionistic_trapezoidal_fuzzy_number_and_its_application_to_multi-criteria_decision_making_problems
Wang, L., Wang, Y.-M., & Martínez, L. (2017). A group decision method based on prospect theory for emergency situations. Information Sciences, 418, 119–135. https://doi.org/10.1016/j.ins.2017.07.037
Wang, S., Wei, G., Wu, J., Wei, C., & Guo, Y. (2021). Model for selection of hospital constructions with probabilistic linguistic GRP method. Journal of Intelligent & Fuzzy Systems, 40(1), 1245–1259. https://doi.org/10.3233/JIFS-201543
Wei, C., Wu, J., Guo, Y., & Wei, G. (2021a). Green supplier selection based on CODAS method in probabilistic uncertain linguistic environment. Technological and Economic Development of Economy, 27(3), 530–549. https://doi.org/10.3846/tede.2021.14078
Wei, G., Wu, J., Guo, Y., Wang, J., & Wei, C. (2021b). An extended COPRAS model for multiple attribute group decision making based on single-valued neutrosophic 2-tuple linguistic environment. Technological and Economic Development of Economy, 27(2), 353–368. https://doi.org/10.3846/tede.2021.14057
Yoon, K., & Hwang, C. L. (1981). TOPSIS (technique for order preference by similarity to ideal solution) – a multiple attribute decision making. In Multiple attribute decision making: Methods and applications, a state-of-the-at survey. Springer Verlag.
Zhang, Y., Wei, G., Guo, Y., & Wei, C. (2021). TODIM method based on cumulative prospect theory for multiple attribute group decision‐making under 2‐tuple linguistic Pythagorean fuzzy environment. International Journal of Intelligent Systems, 36(6), 2548–2571. https://doi.org/10.1002/int.22393
Zhao, M., Wei, G., Wei, C., & Guo, Y. (2021a). CPT‐TODIM method for bipolar fuzzy multi‐attribute group decision making and its application to network security service provider selection. International Journal of Intelligent Systems, 36(5), 1943–1969. https://doi.org/10.1002/int.22367
Zhao, M., Wei, G., Wei, C., Wu, J., & Wei, Y. (2021b). Extended CPT-TODIM method for intervalvalued intuitionistic fuzzy MAGDM and its application to urban ecological risk assessment. Journal of Intelligent & Fuzzy Systems, 40(3), 4091–4106. https://doi.org/10.3233/JIFS-200534
Zhao, M., Wei, G., Wu, J., Guo, Y., & Wei, C. (2021c). TODIM method for multiple attribute group decision making based on cumulative prospect theory with 2‐tuple linguistic neutrosophic sets. International Journal of Intelligent Systems, 36(3), 1199–1222. https://doi.org/10.1002/int.22338
Zhao, M. W., Wei, G. W., Wei, C., & Wu, J. (2021d). Pythagorean fuzzy TODIM method based on the cumulative prospect theory for MAGDM and its application on risk assessment of science and technology projects. International Journal of Fuzzy Systems. https://doi.org/10.1007/s40815-020-00986-8