Skip to main content

A Voting Mechanism Designed for Talent Shows in Mass Media: Weighted Preference of Group Decision Makers in Social Networks Using Fuzzy Measures and Choquet Integral

Abstract

As the development of social networks tends to shape people’s view about choices, decision making theories are challenged by numerous unprecedented difficulties, from both the theories and practice. One hot topic is how to design a voting mechanism for talent shows in mass media that not only attracts public attention but also reflects an objective and fair principle. Weighted voting, where the voting power of a representative is proportional to the population in his or her district, has been widely adopted in legislative selections and talent show competitions. However, weighted voting system may cause disenfranchisement of some representatives and reduce the entertainment and interest of talent shows because of the ignorance of complex interactions among the representatives. In this paper, possible interactions among representatives are analyzed by investigating the associated social networks and subsequently some fuzzy measures are utilized to quantify these interactions. Specifically, the weights determination model is adopted in this situation for defining fuzzy measures to avoid the disenfranchisement, and a multiple-group hierarchy decision model is developed to solve social network group decision making problems where the Choquet integral is employed to reduce the impact from synergy and redundancy between representatives. Moreover, a voting mechanism for talent shows in mass media is provided. Finally, an illustrative example, and a close look at the current algorithmic issues and future trends from different angles are provided.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

Notes

  1. 1.

    https://ent.163.com/15/0303/10/AJPCQQRD00032DGD.html.

    https://www.digitaling.com/articles/13686.html Quoted on 10-17-2019.

  2. 2.

    https://walkthechat.com/5-wechat-survey-tools-to-help-you-learn-more-about-your-followers/ Quoted on 10-17-2019 (Wu et al. 2019a).

  3. 3.

    Semiorder refers to a partially ordered relation. Semiorder or "partial order" is used as an indication that not every pair of elements in a poset needs to be comparable. That is, there may be pairs of elements for which neither element precedes the other in the poset.

References

  1. Alonso S et al (2013) A linguistic consensus model for Web 2.0 communities. Appl Soft Comput 13(1):149–157

    Article  Google Scholar 

  2. Angilella S et al (2016) Robust ordinal regression and stochastic multiobjective acceptability analysis in multiple criteria hierarchy process for the Choquet integral preference model. Omega 63:154–169

    Article  Google Scholar 

  3. Atzmueller M, Doerfel S, Mitzlaff F (2016) Description-oriented community detection using exhaustive subgroup discovery. Inf Sci 329:965–984

    Article  Google Scholar 

  4. Bai L et al (2017) Fast graph clustering with a new description model for community detection. Inf Sci 388–389:37–47

    Article  Google Scholar 

  5. Beliakov G, James S, Wu J-Z (2020) Value and Interaction Indices. Discrete fuzzy measures: computational aspects 2020. Springer, Cham, pp 55–73

    Chapter  Google Scholar 

  6. Bottero M et al (2018) On the Choquet multiple criteria preference aggregation model: theoretical and practical insights from a real-world application. Eur J Oper Res 271(1):120–140

    Article  Google Scholar 

  7. Capuano N et al (2018) Fuzzy Group Decision Making for influence-aware recommendations. Comput Hum Behav 101:371–379

    Article  Google Scholar 

  8. Cherubini U, Lunga GD (2001) Liquidity and credit risk. Appl Math Finance 8(2):79–95

    Article  Google Scholar 

  9. Chiţescu I, Plăviţu A (2017) Computing Choquet integrals. Fuzzy Sets Syst 327:48–68

    Article  Google Scholar 

  10. Choquet G (1954) Theory of capacities. Annales de l'institut Fourier 5:131–295

    Article  Google Scholar 

  11. Dong Y et al (2018) Consensus reaching in social network group decision making: Research paradigms and challenges. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2018.06.036

    Article  Google Scholar 

  12. Dow J, da Costa Werlang SR (1992) Uncertainty aversion, risk aversion, and the optimal choice of portfolio. Econometrica 60:197–204

    Article  Google Scholar 

  13. Grabisch M (1996) The application of fuzzy integrals in multicriteria decision making. Eur J Oper Res 89(3):445–456

    Article  Google Scholar 

  14. Grabisch M (1997) k-Order additive discrete fuzzy measures and their representation. Fuzzy Sets Syst 92(2):167–189

    Article  Google Scholar 

  15. Grabisch M, Labreuche C (2016) Fuzzy measures and integrals in MCDA. In: Greco S, Ehrgott M, Figueira JR (eds) Multiple criteria decision analysis: state of the art surveys. Springer, New York, pp 553–603

    Chapter  Google Scholar 

  16. Herrera-Viedma E et al (2014) A review of soft consensus models in a fuzzy environment. Inf Fusion 17:4–13

    Article  Google Scholar 

  17. Horanská Ľ, Šipošová A (2018) A generalization of the discrete Choquet and Sugeno integrals based on a fusion function. Inf Sci 451–452:83–99

    Article  Google Scholar 

  18. Liu Y-H (2009) Pricing fuzzy vulnerable options and risk management. Expert Syst Appl 36(10):12188–12199

    Article  Google Scholar 

  19. Liu Y et al (2017) A trust induced recommendation mechanism for reaching consensus in group decision making. Knowl-Based Syst 119:221–231

    Article  Google Scholar 

  20. Liyan H, Zheng C (2005) Fuzzy options with application to default risk analysis for municipal bonds in China. Nonlinear Anal Theory Methods Appl 63(5):2353–2365

    Google Scholar 

  21. Lourenzutti R, Krohling RA, Reformat MZ (2017) Choquet based TOPSIS and TODIM for dynamic and heterogeneous decision making with criteria interaction. Inf Sci 408:41–69

    Article  Google Scholar 

  22. Magoč T, Wang X, Modave F (2010) Application of fuzzy measures and interval computation to financial portfolio selection. Int J Intell Syst 25(7):621–635

    Google Scholar 

  23. Marichal J-L (2002) Entropy of discrete Choquet capacities. Eur J Oper Res 137(3):612–624

    Article  Google Scholar 

  24. Marichal J-L, Roubens M (2000) Determination of weights of interacting criteria from a reference set. Eur J Oper Res 124(3):641–650

    Article  Google Scholar 

  25. Murofushi T, Sugeno M (1989) An interpretation of fuzzy measures and the Choquet integral as an integral with respect to a fuzzy measure. Fuzzy Sets Syst 29(2):201–227

    Article  Google Scholar 

  26. Murofushi T, Soneda S (1993) Techniques for reading fuzzy measures (III): interaction index. In: Proceedings of the 9th fuzzy systems symposium

  27. Peng S et al (2018) Influence analysis in social networks: a survey. J Netw Comput Appl 106:17–32

    Article  Google Scholar 

  28. Pérez LG et al (2016) Modelling influence in group decision making. Soft Comput 20(4):1653–1665

    Article  Google Scholar 

  29. Reed M (2015) Social network influence on consistent choice. J Choice Model 17:28–38

    Article  Google Scholar 

  30. Sadovykh V, Sundaram D, Piramuthu S (2015) Do online social networks support decision-making? Decis Support Syst 70:15–30

    Article  Google Scholar 

  31. Schmeidler D (1989) Subjective probability and expected utility without additivity. Econometrica 57:571–587

    Article  Google Scholar 

  32. Shapley LS (1953) A value for n-person games. In: Kuhn HW, Tucker AW (eds) Contributions to the theory of games (AM-28), annals of mathematical studies. Princeton University Press, Princeton, vol II, pp 307–317

    Google Scholar 

  33. Sugeno M (1974) Theory of fuzzy integrals and its applications. In: Tokyo Institute of Technology, Japan

  34. Torra V, Narukawa Y (2016) Numerical integration for the Choquet integral. Inf Fusion 31:137–145

    Article  Google Scholar 

  35. Verbraken T et al (2014) Predicting online channel acceptance with social network data. Decis Support Syst 63:104–114

    Article  Google Scholar 

  36. Victor P et al (2011) Practical aggregation operators for gradual trust and distrust. Fuzzy Sets Syst 184(1):126–147

    Article  Google Scholar 

  37. Wu J, Chiclana F, Herrera-Viedma E (2015) Trust based consensus model for social network in an incomplete linguistic information context. Appl Soft Comput 35:827–839

    Article  Google Scholar 

  38. Wu J, Xiong R, Chiclana F (2016) Uninorm trust propagation and aggregation methods for group decision making in social network with four tuple information. Knowl-Based Syst 96:29–39

    Article  Google Scholar 

  39. Wu J et al (2017) A visual interaction consensus model for social network group decision making with trust propagation. Knowl-Based Syst 122:39–50

    Article  Google Scholar 

  40. Wu T, Liu X, Liu F (2018a) An interval type-2 fuzzy TOPSIS model for large scale group decision making problems with social network information. Inf Sci 432:392–410

    Article  Google Scholar 

  41. Wu J, Chiclana F, Liao H (2018b) Isomorphic multiplicative transitivity for intuitionistic and interval-valued fuzzy preference relations and its application in deriving their priority vectors. IEEE Trans Fuzzy Syst 26(1):193–202

    Article  Google Scholar 

  42. Wu J et al (2019a) A trust propagation and collaborative filtering based method for incomplete information in social network group decision making with type-2 linguistic trust. Comput Ind Eng 127:853–864

    Article  Google Scholar 

  43. Wu J et al (2019b) An attitudinal consensus degree to control the feedback mechanism in group decision making with different adjustment cost. Knowl-Based Syst 164:265–273

    Article  Google Scholar 

  44. Wu J et al (2019c) An attitudinal trust recommendation mechanism to balance consensus and harmony in group decision making. In: IEEE transactions on fuzzy systems, pp 1–1

  45. Yager RR (2017) OWA aggregation of multi-criteria with mixed uncertain satisfactions. Inf Sci 417:88–95

    Article  Google Scholar 

  46. Yu Y, Duan W, Cao Q (2013) The impact of social and conventional media on firm equity value: a sentiment analysis approach. Decis Support Syst 55(4):919–926

    Article  Google Scholar 

  47. Zigurs I, Buckland BK (1998) A theory of task/technology fit and group support systems effectiveness. MIS Q 22(3):313–334

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (NSFC) (71871121, 71401078, and 71971121), Top-notch Academic Programs Project of Jiangsu High Education Institutions, and HRSA, U.S. Department of Health and Human Services (No. H49MC0068).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Guo Wei.

Ethics declarations

Conflict of interest

This section is to certify that we have no potential conflict of interest.

Ethical Approval

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

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cai, M., Yan, L., Gong, Z. et al. A Voting Mechanism Designed for Talent Shows in Mass Media: Weighted Preference of Group Decision Makers in Social Networks Using Fuzzy Measures and Choquet Integral. Group Decis Negot (2020). https://doi.org/10.1007/s10726-020-09666-2

Download citation

Keywords

  • Fuzzy measure
  • Social network
  • Interactions
  • Multiple group hierarchy decision
  • Choquet integral