Khalid, Asma and Beg, Ismat (2018): Role of honesty and confined interpersonal influence in modelling predilections. Published in: Soft Computing No. Published on line April 12, 2019
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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.
Item Type: | MPRA Paper |
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Original Title: | Role of honesty and confined interpersonal influence in modelling predilections |
English Title: | Role of honesty and confined interpersonal influence in modelling predilections |
Language: | English |
Keywords: | Honesty; group decision making; social network analysis; confined influence; predilection. |
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 D - Microeconomics > D7 - Analysis of Collective Decision-Making > D71 - Social Choice ; Clubs ; Committees ; Associations D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D81 - Criteria for Decision-Making under Risk and Uncertainty |
Item ID: | 95831 |
Depositing User: | Prof Ismat Beg |
Date Deposited: | 12 Sep 2019 11:42 |
Last Modified: | 26 Sep 2019 13:45 |
References: | Qian, L., Liao, X., & Liu, J. (2017). A social ties-based approach for group decision-making problems with incomplete additive preference relations. Knowledge-Based Systems, 119, 68-86. Beg, I., & Rahid, T. (2017). Modelling uncertainties in multi-criteria decision making using distance measure and TOPSIS for hesitant fuzzy sets. Journal of Artificial Intelligence and Soft Computing Research, 7(2), 103-109. Benferhat, S., Bouraoui, Z., Chaudhry, H., Rahim , M. S., 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, 282-287. Bezdek, , J., Bonnie , S., & 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., Viedma, E. H., & Loia, V. (2018). Fuzzy group decision making with incomplete information guided by social influence. IEEE Transactions on Fuzzy Systems , 1704-1718. 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, 254-258. 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. European Journal of Operational Research, 182 (1), 383-399. DeGroot, M. H. (1974). Reaching a consensus. Journal of the American Statistical Association, 69, 118-121. Friedkin, N. E., & Johnsen, E. C. (1999). Social Influence Networks and Opinion Change. Advances in Group Processes., 16(1), 1-29. Giles, R. (1976). Łukasiewicz logic and fuzzy set theory. International Journal of Man-Machine Studies, 8 (3), 313-327. Hannu, N. (1981). Approaches to collective decision making with fuzzy preference relations. Fuzzy Sets and systems, 6(3), 249-259. Herrera-Viedma, E., Herrera, F., Francisco, C., & Luque, M. (2004). Some issues on consistency of fuzzy preference relations. European journal of operational research, 154 (1), 98-109. John, S., & Carrington, P. J. (2011). The SAGE handbook of social network analysis. SAGE publications. Khalid, A., & Beg, I. (2019). Soft Pedal and Influence-Based Decision Modelling. International Journal of Fuzzy Systems, 1-10. Mitchell, H. B., & D, E. D. (1997). A modified OWA operator and its use in lossless DPCM image compression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 5 (04), 429-436. Pérez, L. G., Mata, F., Chiclana, F., Kou, G., & Herrera-Viedma, E. (2016). Modelling influence in group decision making. Soft Computing, 20 (4), 1653-1665. Siegfried, W. (1983). A general concept of fuzzy connectives, negations and implications based on t-norms and t-conorms. Fuzzy Sets and Systems, 11(1-3), 115-134. Stanley, W., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge university press, 8. Tanino, T. (1984). Fuzzy preference orderings in group decision making. Fuzzy sets and Systems, 12(2), 117-131. Yager, R. R. (1983). Quantifiers in the formulation of multiple objective decision functions. Information Sciences, 31 (2), 107-139. 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. (2003). Induced aggregation operators. Fuzzy Sets and Systems, 137 (1), 59-69. Yager, R. R., & Dimitar, F. P. (1999). Induced ordered weighted averaging operators. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29(2), 141-150. Zadeh, L. A. (1983). A computational approach to fuzzy quantifiers in natural languages. Computers and Mathematics with applications, 9(1), 149-184. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/95831 |