Advertisement

Role of honesty and confined interpersonal influence in modelling predilections

  • Asma KhalidEmail author
  • Ismat Beg
Methodologies and Application
  • 13 Downloads

Abstract

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.

Keywords

Honesty Group decision-making Social network analysis Confined influence Predilection 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Research does not directly involve human participants. Informed consent is ensured.

References

  1. 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
  2. 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
  3. Bezdek J, Bonnie S, Spillman R (1978) A fuzzy relation space for group decision theory. Fuzzy Sets Syst 1(4):255–268MathSciNetzbMATHGoogle Scholar
  4. 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
  5. 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
  6. Chiclana F, Herrera-Viedma E, Francisco H, Alonso S (2007) Some induced ordered weighted averaging operators and their use for solving group decision-making problems based on fuzzy preference relations. Eur J Oper Res 182(1):383–399zbMATHGoogle Scholar
  7. DeGroot MH (1974) Reaching a consensus. J Am Stat Assoc 69:118–121zbMATHGoogle Scholar
  8. Friedkin NE, Johnsen EC (1999) Social influence networks and opinion change. Adv Group Process 16(1):1–29Google Scholar
  9. Giles R (1976) Łukasiewicz logic and fuzzy set theory. Int J Man Mach Stud 8(3):313–327MathSciNetzbMATHGoogle Scholar
  10. Hannu N (1981) Approaches to collective decision making with fuzzy preference relations. Fuzzy Sets Syst 6(3):249–259MathSciNetzbMATHGoogle Scholar
  11. Herrera-Viedma E, Herrera F, Francisco C, Luque M (2004) Some issues on consistency of fuzzy preference relations. Eur J Oper Res 154(1):98–109MathSciNetzbMATHGoogle Scholar
  12. John S, Carrington PJ (2011) The SAGE handbook of social network analysis. SAGE Publications, Thousand OaksGoogle Scholar
  13. Khalid A, Beg I (2019) Soft pedal and influence-based decision modelling. Int J Fuzzy Syst.  https://doi.org/10.1007/s40815-018-00600-y MathSciNetGoogle Scholar
  14. Mitchell HB, Estrakh DD (1997) A modified OWA operator and its use in lossless DPCM image compression. Int J Uncertain Fuzziness Knowl Based Syst 5(04):429–436zbMATHGoogle Scholar
  15. 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
  16. 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
  17. Siegfried W (1983) A general concept of fuzzy connectives, negations and implications based on t-norms and t-conorms. Fuzzy Sets Syst 11(1–3):115–134MathSciNetzbMATHGoogle Scholar
  18. Stanley W, Faust K (1994) Social network analysis: methods and applications, 8. Cambridge University Press, CambridgezbMATHGoogle Scholar
  19. Tanino T (1984) Fuzzy preference orderings in group decision making. Fuzzy Sets Syst 12(2):117–131MathSciNetzbMATHGoogle Scholar
  20. Yager RR (1983) Quantifiers in the formulation of multiple objective decision functions. Inf Sci 31(2):107–139MathSciNetzbMATHGoogle Scholar
  21. Yager RR (1988) On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans Syst Man Cybernet 18(1):183–190MathSciNetzbMATHGoogle Scholar
  22. Yager RR (2003) Induced aggregation operators. Fuzzy Sets Syst 137(1):59–69MathSciNetzbMATHGoogle Scholar
  23. Yager RR, Dimitar FP (1999) Induced ordered weighted averaging operators. IEEE Trans Syst Man Cybernet Part B (Cybernet) 29(2):141–150Google Scholar
  24. Zadeh LA (1983) A computational approach to fuzzy quantifiers in natural languages. Comput Math Appl 9(1):149–184MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Center for Mathematics and Statistical SciencesLahore School of EconomicsLahorePakistan

Personalised recommendations