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.
BegI., RashidT. and JamilR.N., Human attitude analysis based on fuzzy soft differential equations with Bonferroni mean, Computational and Applied Mathematics37(3) (2018), 2632–2647.
2.
BezdekJ.C., SpillmanB. and SpillmanR., A fuzzy relation space for group decision theory, Fuzzy Sets and Systems1(4) (1978), 255–268.
3.
CapuanoN., ChiclanaF., FujitaH., Herrera-ViedmaE. and LoiaV., Fuzzy group decision making with incomplete information guided by social inuence, IEEE Transactions on Fuzzy Systems (2017), doi:10.1109/TFUZZ.2017.2744605.
4.
DeGrootM.H.Reaching a consensus, Journal of the American Statistical Association69(345) (1974), 118–121.
5.
DongY., ZhanM., KouG., DingZ. and LiangH., A survey on the fusion process in opinion dynamics, Information Fusion43 (2018), 57–65.
6.
DongY., ZhaQ., ZhangH., KouG., FujitaH., ChiclanaF. and Herrera-ViedmaE., Consensus reaching in social network group decision making: Research paradigms and challenges. Knowledge-Based Systems, (2018).
7.
DongY., ZhaoS., ZhangH., ChiclanaF. and Herrera-ViedmaE., A Self-Management Mechanism for Noncooperative Behaviors in Large-Scale Group Consensus Reaching Processes, IEEE Transactions on Fuzzy Systems26(6) (2018), 3276–3288.
8.
DongY., DingZ., MartínezL. and HerreraF., Managing consensus based on leadership in opinion dynamics, Information Sciences397 (2017), 187–205.
9.
FriedkinN. and JohnsenE., Social inuence network and opinion change, Advances in Group Processes16(1) (1999), 1–29.
10.
KhalidA. and AwaisM.M., Comparing ranking methods: Complete RCI pref-erence and multiplicative preference relations, Journal of Intelligent and Fuzzy Systems27(2) (2014), 849–861.
11.
LiangQ., LiaoX. and LiuJ., A social ties-based approach for group decision-making problems with incomplete additive preference relations, Knowledge-Based Systems119 (2017), 68–86.
12.
NurmiH., Approaches to collective decision making with fuzzy preference relations, Fuzzy Sets and Systems6(3) (1981), 249–259.
13.
PérezI.J., CabrerizoF.J., AlonsoS., DongY.C., ChiclanaF. and Herrera-ViedmaE., On dynamic consensus processes in group decision making problems, Information Sciences459 (2018), 20–35.
14.
PérezL.G., MataF., ChiclanaF., KouG. and Herrera-ViedmaE., Modelling inuence in group decision making, Soft Computing20(4) (2016), 1653–1665.
15.
ScottJ. and CarringtonP.J., The SAGE handbook of social network analysis. SAGE publications. (2011).
16.
TaninoT., Fuzzy preference orderings in group decision making, Fuzzy Sets and Systems12(2) (1984), 117–131.
17.
TaninoT., Fuzzy preference relations in group decision making. In Non-conventional preference relations in decision making (pp.54–71). Springer Hei-delberg, (1988).
18.
UrenaR., KouG., DongY., ChiclanaF. and Herrera-ViedmaE., A review on trust propagation and opinion dynamics in social networks and group decision making frameworks, Information Sciences478 (2019), 461–475.
19.
WassermanS. and FaustK., Social network analysis: Methods and Applications (Vol. 8). Cambridge University Press. (1994).
20.
YagerR.R., On ordered weighted averaging aggregation operators in multicriteria decisionmaking, IEEE Transactions on systems, Man and Cybernetics18(1) (1988), 183–190.
21.
YagerR.R., Quantifier guided aggregation using OWA operators, International Journal of Intelligent Systems11(1) (1996), 49–73.
22.
YagerR.R., Quantifiers in the formulation of multiple objective decision functions, Information Sciences31(2) (1983), 107–139.
23.
ZadehL.A., A computational approach to fuzzy quantifiers in natural languages, Computers and Mathematics with Applications9(1) (1983), 149–184.
24.
ZhangB., LiangH. and ZhangG., Reaching a consensus with minimum adjustment in MAGDM with hesitant fuzzy linguistic term sets, Information Fusion42 (2018), 12–23.
25.
ZhangB., DongY. and Herrera-ViedmaE., Group decision making with het-erogeneous preference structures: An automatic mechanism to support consensus reaching, Group Decision and Negotiation (2019), 1–33.
26.
ZhangH., DongY., ChiclanaF. and YuS., Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design, European Journal of Operational Research275(2) (2019), 580–598.
