Khalid, Asma and Beg, Ismat (2018): Influence model of evasive decision makers. Forthcoming in: Journal of Intelligent and Fuzzy Systems (2020)
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Abstract
The aim of this paper is to introduce the notion of truthfulness in an influence based decision making model. An expert may submit his opinions truthfully or he may dismantle the original situation by undermining the actual opinion, such a decision maker is called an evasive decision maker or an almost truthful decision maker in this paper. It is assumed that experts in the panel are dignified members hence even though they are not habitual liars, they are either "almost truthful" or evasive. To measure their degree of truthfulness, we use the information provided by them in the form of preference relations. We use this information to state the foundation of influence model of evasive decision makers. Finally, a ranking method is proposed to find best possible solutions.
Item Type: | MPRA Paper |
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Original Title: | Influence model of evasive decision makers |
English Title: | Influence model of evasive decision makers |
Language: | English |
Keywords: | Truthfulness; group decision making ; social influence networks; additive reciprocity |
Subjects: | C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C44 - Operations Research ; Statistical Decision Theory C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling D - Microeconomics > D7 - Analysis of Collective Decision-Making |
Item ID: | 95493 |
Depositing User: | Prof Ismat Beg |
Date Deposited: | 12 Aug 2019 09:31 |
Last Modified: | 29 Sep 2019 22:11 |
References: | Beg, I., Rashid, T., and Jamil, R. N. (2018). Human attitude analysis based on fuzzy soft differential equations with Bonferroni mean. Computational and Applied Mathematics, 37(3), 2632-2647. Bezdek, J. C., Spillman, B., and Spillman, R. (1978). A fuzzy relation space for group decision theory. Fuzzy Sets and Systems, 1(4), 255-268. Capuano, N., Chiclana, F., Fujita, H., Herrera-Viedma, E., and Loia, V. (2017). Fuzzy group decision making with incomplete information guided by social influence. IEEE Transactions on Fuzzy Systems. DOI 10.1109/TFUZZ.2017.2744605 DeGroot, M. H. (1974). Reaching a consensus. Journal of the American Statistical Association, 69(345), 118-121. Dong, Y., Zhan, M., Kou, G., Ding, Z., and Liang, H. (2018). A survey on the fusion process in opinion dynamics. Information Fusion, 43, 57-65. Dong, Y., Zha, Q., Zhang, H., Kou, G., Fujita, H., Chiclana, F., and Herrera-Viedma, E. (2018). Consensus reaching in social network group decision making: Research paradigms and challenges. Knowledge-Based Systems. Dong, Y., Zhao, S., Zhang, H., Chiclana, F., and Herrera-Viedma, E. (2018). A Self-Management Mechanism for Noncooperative Behaviors in Large-Scale Group Consensus Reaching Processes. IEEE Transactions on Fuzzy Systems, 26(6), 3276-3288. Dong, Y., Ding, Z., MartÃnez, L., and Herrera, F. (2017). Managing consensus based on leadership in opinion dynamics. Information Sciences, 397, 187-205. Friedkin N, Johnsen E (1999). Social influence network and opinion change, Advances in Group Processes, 16(1):1--29. Khalid, A., and Awais, M. M. (2014). Comparing ranking methods: complete RCI preference and multiplicative preference relations. Journal of Intelligent and Fuzzy Systems, 27(2), 849-861. Liang, Q., Liao, X., and Liu, J. (2017). A social ties-based approach for group decision-making problems with incomplete additive preference relations. Knowledge-Based Systems, 119, 68-86. Nurmi, H. (1981). Approaches to collective decision making with fuzzy preference relations. Fuzzy Sets and Systems, 6(3), 249-259. Pérez, I. J., Cabrerizo, F. J., Alonso, S., Dong, Y. C., Chiclana, F., and Herrera-Viedma, E. (2018). On dynamic consensus processes in group decision making problems. Information Sciences, 459, 20-35. Pérez, L. G., Mata, F., Chiclana, F., Kou, G., and Herrera-Viedma, E. (2016). Modelling influence in group decision making. Soft Computing, 20(4), 1653-1665. Scott, J., and Carrington, P. J. (2011). The SAGE handbook of social network analysis. SAGE publications. Tanino, T. (1984). Fuzzy preference orderings in group decision making. Fuzzy sets and systems, 12(2), 117-131. Tanino, T. (1988). Fuzzy preference relations in group decision making. In Non-conventional preference relations in decision making (pp. 54-71). Springer Berlin Heidelberg. Urena, R., Kou, G., Dong, Y., Chiclana, F., and Herrera-Viedma, E. (2019). A review on trust propagation and opinion dynamics in social networks and group decision making frameworks. Information Sciences, 478, 461-475. Wasserman, S., and Faust, K. (1994). Social network analysis: Methods and Applications (Vol. 8). Cambridge University Press. Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on systems, Man, and Cybernetics, 18(1), 183-190. Yager, R. R. (1996). Quantifier guided aggregation using OWA operators. International Journal of Intelligent Systems, 11(1), 49-73. Yager, R. R. (1983). Quantifiers in the formulation of multiple objective decision functions. Information Sciences, 31(2), 107-139. Zadeh, L. A. (1983). A computational approach to fuzzy quantifiers in natural languages. Computers and Mathematics with Applications, 9(1), 149-184. Zhang, B., Liang, H., and Zhang, G. (2018). Reaching a consensus with minimum adjustment in MAGDM with hesitant fuzzy linguistic term sets. Information Fusion, 42, 12-23. Zhang, B., Dong, Y., and Herrera-Viedma, E. (2019). Group decision making with heterogeneous preference structures: An automatic mechanism to support consensus reaching. Group Decision and Negotiation, 1-33. Zhang, H., Dong, Y., Chiclana, F., and Yu, S. (2019). Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design. European Journal of Operational Research, 275(2), 580-598. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/95493 |