Role of honesty and confined interpersonal influence in modelling predilections
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Classical models of decision-making do not incorporate for the role of influence and honesty that affects the process. This paper develops on the theory of influence in social network analysis. We study the role of influence and honesty of individual experts on collective outcomes. It is assumed that experts have the tendency to improve their initial predilection for an alternative, over the rest, if they interact with one another. It is suggested that this revised predilection may not be proposed with complete honesty by the expert. Degree of honesty is computed from the preference relation provided by the experts. This measure is dependent on average fuzziness in the relation and its disparity from an additive reciprocal relation. Moreover, an algorithm is introduced to cater for incompleteness in the adjacency matrix of interpersonal influences. This is done by analysing the information on how the expert has influenced others and how others have influenced the expert.
KeywordsHonesty Group decision-making Social network analysis Confined influence Predilection
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Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
Research does not directly involve human participants. Informed consent is ensured.
- Beg I, Rahid T (2017) Modelling uncertainties in multi-criteria decision making using distance measure and TOPSIS for hesitant fuzzy sets. J Artif Intell Soft Comput Res 7(2):103–109Google Scholar
- Benferhat S, Bouraoui Z, Chaudhry H, Rahim MS, Tabia K, Telli A (2016) Characterizing non-defeated repairs in inconsistent lightweight ontologies. In: 2016 12th international conference on signal-image technology & internet-based systems (SITIS). IEEE, pp 282–287Google Scholar
- Capuano N, Chiclana F, Fujita H, Viedma EH, Loia V (2018) Fuzzy group decision making with incomplete information guided by social influence. IEEE Trans Fuzzy Syst 26(3):1704–1718Google Scholar
- Chaudhry H, Karim T, Abdul Rahim S, BenFerhat S (2017). Automatic annotation of traditional dance data using motion features. In: 2017 international conference on digital arts, media and technology (ICDAMT). IEEE, pp 254–258Google Scholar
- Friedkin NE, Johnsen EC (1999) Social influence networks and opinion change. Adv Group Process 16(1):1–29Google Scholar
- John S, Carrington PJ (2011) The SAGE handbook of social network analysis. SAGE Publications, Thousand OaksGoogle Scholar
- Pérez LG, Mata F, Chiclana F, Kou G, Herrera-Viedma E (2016) Modelling influence in group decision making. Soft Comput 20(4):1653–1665Google Scholar
- Qian L, Liao X, Liu J (2017) A social ties-based approach for group decision-making problems with incomplete additive preference relations. Knowl-Based Syst 119:68–86Google Scholar
- Yager RR, Dimitar FP (1999) Induced ordered weighted averaging operators. IEEE Trans Syst Man Cybernet Part B (Cybernet) 29(2):141–150Google Scholar