Adaptive consensus building in emergency group decision-making with hesitant fuzzy linguistic information: a perspective based on disappointment theory
Restricted accessResearch articleFirst published online April, 2022
Adaptive consensus building in emergency group decision-making with hesitant fuzzy linguistic information: a perspective based on disappointment theory
In this article, an adaptive consensus model that considers individual disappointment emotion is proposed for emergency multi-attribute group decision-making (MAGDM) problems with hesitant fuzzy linguistic information. Subsequently, it is applied to choose the optimal emergency alternative(s) for the prevention and control of COVID-19 on a college campus. The main innovations and contributions of this article are as follows: (a) Individual modified perceived utility (MPU) based on disappointment theory is integrated into the determination of attribute weights and construction of consensus reaching process (CRP). (b) The MPU-based individual contribution degree and the MPU-based soft group consensus degree are developed. (c) The new emergency CRP mechanism not only considers the rewards and penalties of expert weights but also regards the adaptive updating of attribute weights. Compared with the existing emergency MAGDM models in a hesitant fuzzy linguistic environment, the proposed consensus model has some advantages in improving consensus efficiency and simulating uncertain psychological behavior.
BellD. E. (1985). Disappointment in decision making under uncertainty. Operations Research, 33(1), 1–27.
2.
Ben-AriehD.ChenZ. (2006). Linguistic-labels aggregation and consensus measure for autocratic decision making using group recommendations. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 36(3), 558–568.
3.
CaoJ.ZhuL.HanH.ZhuX. (2018). Modern emergency management. Springer.
4.
ChenS. M.TsaiB. H. (2015). Autocratic decision making using group recommendations based on intervals of linguistic terms and likelihood-based comparison relations. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(2), 250–259.
5.
ChenX.LiangH.GaoY.XuW. (2020). A method based on the disappointment almost stochastic dominance degree for the multi-attribute decision making with linguistic distributions. Information Fusion, 54, 10–20.
6.
ChengS. H. (2018). Autocratic decision making using group recommendations based on hesitant fuzzy sets for green hotels selection and bidders selection. Information Sciences, 467, 604–617.
7.
CheungK. C.ChongW. F.YamS. C. P. (2015). The optimal insurance under disappointment theories. Insurance: Mathematics and Economics, 64, 77–90.
8.
del MoralM. J.ChiclanaF.TapiaJ. M.Herrera-ViedmaE. (2018). A comparative study on consensus measures in group decision making. International Journal of Intelligent Systems, 33(8), 1624–1638.
9.
DelquiéP.CilloA. (2006). Disappointment without prior expectation: A unifying perspective on decision under risk. Journal of Risk and Uncertainty, 33(3), 197–215.
10.
DongY.ChenX.HerreraF. (2015). Minimizing adjusted simple terms in the consensus reaching process with hesitant linguistic assessments in group decision making. Information Sciences, 297, 95–117.
11.
DongY.LuoN.LiangH. (2015). Consensus building in multiperson decision making with heterogeneous preference representation structures: A perspective based on prospect theory. Applied Soft Computing, 35, 898–910.
12.
FanZ. P.LiM. Y.ZhangX. (2018). Satisfied two-sided matching: A method considering elation and disappointment of agents. Soft Computing, 22(21), 7227–7241.
13.
FuC.XuD. L.XueM. (2018). Determining attribute weights for multiple attribute decision analysis with discriminating power in belief distributions. Knowledge-Based Systems, 143, 127–141.
14.
González-ArteagaT.de Andrés CalleR.ChiclanaF. (2016). A new measure of consensus with reciprocal preference relations: The correlation consensus degree. Knowledge-based Systems, 107, 104–116.
15.
GouX.XuZ. (2016). Novel basic operational laws for linguistic terms, hesitant fuzzy linguistic term sets and probabilistic linguistic term sets. Information Sciences, 372, 407–427.
16.
GouX.XuZ.LiaoH. (2017). Multiple criteria decision making based on Bonferroni means with hesitant fuzzy linguistic information. Soft Computing, 21(21), 6515–6529.
17.
GulF. (1991). A theory of disappointment aversion. Econometrica: Journal of the Econometric Society, 59(3), 667–686.
18.
GuoX.EgozcueM.WongW. K. (2020). Production theory under price uncertainty for firms with disappointment aversion. International Journal of Production Research. Advance online publication. https://doi.org/10.1080/00207543.2020.1733699
19.
HaoJ.ChiclanaF. (2020). Attitude quantifier based possibility distribution generation method for hesitant fuzzy linguistic group decision making. Information Sciences, 518, 341–360.
20.
HwangC. L.YoonK. (1981). Multiple attribute decision making: A state of the art survey. Lecture Notes in Economics and Mathematical Systems, 186, 58–191.
21.
JiaJ.DyerJ. S.ButlerJ. C. (2001). Generalized disappointment models. Journal of Risk and Uncertainty, 22(1), 59–78.
22.
LacianaC. E.WeberE. U. (2008). Correcting expected utility for comparisons between alternative outcomes: A unified parameterization of regret and disappointment. Journal of Risk and Uncertainty, 36(1), 1–17.
23.
Lee RodgersJ.NicewanderW. A. (1988). Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1), 59–66.
24.
LiP.WeiC. (2019). An emergency decision-making method based on DS evidence theory for probabilistic linguistic term sets. International Journal of Disaster Risk Reduction, 37, Article 101178.
25.
LiangY.TuY.JuY.ShenW. (2019). A multi-granularity proportional hesitant fuzzy linguistic TODIM method and its application to emergency decision making. International Journal of Disaster Risk Reduction, 36, Article 101081.
26.
LiaoH.QinR.GaoC.WuX.HafezalkotobA.HerreraF. (2019). Score-HeDLiSF: A score function of hesitant fuzzy linguistic term set based on hesitant degrees and linguistic scale functions: An application to unbalanced hesitant fuzzy linguistic MULTIMOORA. Information Fusion, 48, 39–54.
27.
LiaoH.XuZ.Herrera-ViedmaE.HerreraF. (2018). Hesitant fuzzy linguistic term set and its application in decision making: A state-of-the-art survey. International Journal of Fuzzy Systems, 20(7), 2084–2110.
28.
LiaoH.XuZ.ZengX. J.MerigóJ. M. (2015). Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets. Knowledge-Based Systems, 76, 127–138.
29.
LiuX.WangZ.ZhangS. (2018). A new methodology for hesitant fuzzy emergency decision making with unknown weight information. Complexity, 2018, Article 5145348. https://doi.org/10.1155/2018/5145348
30.
LivetP. (2010). Rational choice, neuroeconomy and mixed emotions. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1538), 259–269.
31.
LoomesG. (1988). Further evidence of the impact of regret and disappointment in choice under uncertainty. Economica, 55(217), 47–62.
32.
LoomesG.SugdenR. (1986). Disappointment and dynamic consistency in choice under uncertainty. The Review of Economic Studies, 53(2), 271–282.
33.
Morente-MolineraJ. A.WuX.MorfeqA.Al-HmouzR.Herrera-ViedmaE. (2020). A novel multi-criteria group decision-making method for heterogeneous and dynamic contexts using multi-granular fuzzy linguistic modelling and consensus measures. Information Fusion, 53, 240–250
34.
PalomaresI.CrosscombeM.ChenZ. S.LawryJ. (2018, October). Dual consensus measure for multi-perspective multi-criteria group decision making [Conference session]. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Institute of Electrical and Electronics Engineers.
35.
PalomaresI.EstrellaF. J.MartínezL.HerreraF. (2014). Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study. Information Fusion, 20, 252–271.
36.
RenZ.LiaoH.LiuY. (2020). Generalized Z-numbers with hesitant fuzzy linguistic information and its application to medicine selection for the patients with mild symptoms of the COVID-19. Computers & Industrial Engineering, 145, Article 106517.
37.
RodriguezR. M.MartinezL.HerreraF. (2012). Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 20(1), 109–119.
38.
SchimmackU.DienerE. (1997). Affect intensity: Separating intensity and frequency in repeatedly measured affect. Journal of Personality and Social Psychology, 73(6), 1313–1329.
39.
SellakH.OuhbiB.FrikhB.IkkenB. (2019). Expertise-based consensus building for MCGDM with hesitant fuzzy linguistic information. Information Fusion, 50, 54–70.
40.
TangM.ZhouX.LiaoH.XuJ.FujitaH.HerreraF. (2019). Ordinal consensus measure with objective threshold for heterogeneous large-scale group decision making. Knowledge-Based Systems, 180, 62–74
41.
TianZ. P.WangJ. Q.ZhangH. Y.WangT. L. (2018). Signed distance-based consensus in multi-criteria group decision-making with multi-granular hesitant unbalanced linguistic information. Computers & Industrial Engineering, 124, 125–138.
42.
TverskyA.KahnemanD. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.
43.
WanS. P.ZouW.DongJ. Y. (2020). Prospect theory based method for heterogeneous group decision making with hybrid truth degrees of alternative comparisons. Computers & Industrial Engineering, 141, Article 106285.
44.
WangJ.WangZ. (2020). Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of China’s prevention and control strategy for the COVID-19 epidemic. International Journal of Environmental Research and Public Health, 17(7), Article 2235.
45.
WangL.LabellaÁ.RodríguezR. M.WangY. M.MartínezL. (2017). Managing non-homogeneous information and experts’ psychological behavior in group emergency decision making. Symmetry, 9(10), Article 234.
46.
WeinerB.RussellD.LermanD. (1979). The cognition-emotion process in achievement-related contexts. Journal of Personality and Social Psychology, 37(7), 1211–1220.
47.
WuT.LiuX.QinJ.HerreraF. (2019). Consensus evolution networks: A consensus reaching tool for managing consensus thresholds in group decision making. Information Fusion, 52, 375–388.
48.
WuX.LiaoH. (2019). A consensus-based probabilistic linguistic gained and lost dominance score method. European Journal of Operational Research, 272(3), 1017–1027.
49.
WuZ.XuJ. (2016). Possibility distribution-based approach for MAGDM with hesitant fuzzy linguistic information. IEEE Transactions on Cybernetics, 46(3), 694–705.
50.
WuZ.XuJ.JiangX.ZhongL. (2019). Two MAGDM models based on hesitant fuzzy linguistic term sets with possibility distributions: VIKOR and TOPSIS. Information Sciences, 473, 101–120.
51.
XiaM. (2018). A hesitant fuzzy linguistic multi-criteria decision-making approach based on regret theory. International Journal of Fuzzy Systems, 20(7), 2135–2143.
XuY.LiuX.XuL. (2020). A dynamic expert contribution-based consensus model for hesitant fuzzy group decision making with an application to water resources allocation selection. Soft Computing, 24(6), 4693–4708.
54.
XuY.WenX.ZhangW. (2018). A two-stage consensus method for large-scale multi-attribute group decision making with an application to earthquake shelter selection. Computers & Industrial Engineering, 116, 113–129.
55.
YanH. B.MaT.HuynhV. N. (2017). On qualitative multi-attribute group decision making and its consensus measure: A probability based perspective. Omega, 70, 94–117.
56.
YuL.LaiK. K. (2011). A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support. Decision Support Systems, 51(2), 307–315
57.
YuW.ZhangZ.ZhongQ. (2019). Consensus reaching for MAGDM with multi-granular hesitant fuzzy linguistic term sets: A minimum adjustment-based approach. Annals of Operations Research. Advance online publication. https://doi.org/10.1007/s10479-019-03432-7
58.
ZeelenbergM.Van DijkW. W.MansteadA. S.vanrdePligtJ. (2000). On bad decisions and disconfirmed expectancies: The psychology of regret and disappointment. Cognition & Emotion, 14(4), 521–541.
59.
ZhangB.LiangH.ZhangG. (2018). Reaching a consensus with minimum adjustment in MAGDM with hesitant fuzzy linguistic term sets. Information Fusion, 42, 12–23.
60.
ZhangH.DongY.Herrera-ViedmaE. (2017). Consensus building for the heterogeneous large-scale GDM with the individual concerns and satisfactions. IEEE Transactions on Fuzzy Systems, 26(2), 884–898.
61.
ZhangL.WangY.ZhaoX. (2018). A new emergency decision support methodology based on multi-source knowledge in 2-tuple linguistic model. Knowledge-Based Systems, 144, 77–87.
62.
ZhangS.ZhuJ.LiuX.ChenY. (2016). Regret theory-based group decision-making with multidimensional preference and incomplete weight information. Information Fusion, 31, 1–13.
63.
ZhangS.ZhuJ.LiuX.ChenY.MaZ. (2017). Adaptive consensus model with multiplicative linguistic preferences based on fuzzy information granulation. Applied Soft Computing, 60, 30–47.
64.
ZhangX. Y.WangJ. Q.HuJ. H. (2018). A consensus approach to multi-granular linguistic MCGDM with hesitant fuzzy linguistic information by using projection. Journal of Intelligent & Fuzzy Systems, 34(3), 1959–1974.
65.
ZhangZ.KouX.PalomaresI.YuW.GaoJ. (2019). Stable two-sided matching decision making with incomplete fuzzy preference relations: A disappointment theory based approach. Applied Soft Computing, 84, Article 105730.
66.
ZhouL.WuX.XuZ.FujitaH. (2018). Emergency decision making for natural disasters: An overview. International Journal of Disaster Risk Reduction, 27, 567–576.
67.
ZhouP.YangX. L.WangX. G.HuB.ZhangL.ZhangW.SiH.-R.ZhuY.LiB.HuangC.-L.ChenH.-D.ChenJ.LuoY.GuoH.JiangR.-D.LiuM.-Q.ChenY.ShenX.-R.WangX.. . . ChenH. D. (2020). A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature, 579(7798), 270–273.
