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The purpose of this study is to examine the influence of different parameters on the legitimacy rate and effective efficiency of crowd decision-making and to guide decision-making in real life. In this paper, a crowd decision representation method based on the preference domain is proposed for the large-scale simulation implementation of crowd decision in a crowd intelligence network, a simulation modeling is performed for the members participating in the decision, and a formal propulsion algorithm is perfected. Lastly, the influence of key parameters on the decision results is analyzed through a large-scale simulation experiment. This study analyzes the influence of key parameters, such as the number of candidates, number of voters, and voting legitimacy rate reference value, on the decision-making results and summarizes the selection range of key parameters under different results. Through the simulation experiment of crowd decision-making, this paper provides inspiration for researchers to explore the parameter sensitivity of crowd decision-making and provides guidance for crowd decision-making in social life.
The purpose of this study is to examine the influence of different parameters on the legitimacy rate and effective efficiency of crowd decision-making and to guide decision-making in real life. In this paper, a crowd decision representation method based on the preference domain is proposed for the large-scale simulation implementation of crowd decision in a crowd intelligence network, a simulation modeling is performed for the members participating in the decision, and a formal propulsion algorithm is perfected. Lastly, the influence of key parameters on the decision results is analyzed through a large-scale simulation experiment. This study analyzes the influence of key parameters, such as the number of candidates, number of voters, and voting legitimacy rate reference value, on the decision-making results and summarizes the selection range of key parameters under different results. Through the simulation experiment of crowd decision-making, this paper provides inspiration for researchers to explore the parameter sensitivity of crowd decision-making and provides guidance for crowd decision-making in social life.
T. Ming and H. Liao, From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey, Omega, vol. 100, p. 102141, 2021.
Y. Chai, C. Miao, B. Sun, Y. Zheng, and Q. Li, Crowd science and engineering: Concept and research framework, International Journal of Crowd Science, vol. 1, no. 1, pp. 2–8, 2017.
S. Brams, Voting procedures, Handbook of Game Theory with Economic Applications, vol. 2, pp. 1055–1089, 1994.
J. Pang, J. Liang, and P. Song, An adaptive consensus method for multi-attribute group decision making under uncertain linguistic environment, Applied Soft Computing, vol. 58, pp. 339–353, 2017.
D. Luo, H. Zhang, and D. Sun, Grey multi-attribute group decision making method with consideration of psychological behavior of decision makers, Control and Decision, vol. 36, no. 7, pp. 1779–1785, 2021.
M. Adida, L. Clark, P. Pomietto, A. Kaladjian, N. Besnier, J. -M. Azorin, R. Jeanningros, and G. M. Goodwin, Lack of insight may predict impaired decision making in manic patients, Bipolar Disorders, vol. 10, no. 7, pp. 829–837, 2008.
F. C. Brodbeck, R. Kerschreiter, A. Mojzisch, and S. Schulz-Hardt, Group decision making under conditions of distributed knowledge: The information asymmetries model, The Academy of Management Review, vol. 32, no. 2, pp. 459–479, 2007.
P. C. Fishburn, A. H. Murphy, and H. H. Isaacs, Sensitivity of decisions to probability estimation errors: A reexamination, Operations Research, vol. 16, no. 2, pp. 254–267, 1968.
T. Masuda, Hierarchical sensitivity analysis of the priority used in analytic hierarchy process, Systems Science, vol. 21, no. 2, pp. 415–427, 1990.
R. L. Armacost and J. C. Hosseini, Identification of determinant attributes using the analytic hierarchy process, Journal of the Academy of Marketing Science, vol. 22, no. 4, pp. 383–392, 1994.
J. L. Ringuest, S. B. Graves, and R. H. Case, Mean-gini analysis in R&D portfolio selection,European Journal of Operational Research, vol. 154, no. 1, pp. 157–169, 2004.
J. Zuo, Research on theoretical method of weight vector sensitivity analysis in multi-objective decision making, (in Chinese), Systems Engineering-Theory & Practice, vol. 3, pp. 1–11, 1987.
C. Mészáros and T. Rapcsák, On sensitivity analysis for a class of decision systems, Decision Support Systems, vol. 16, no. 3, pp. 231–240, 1996.
Y. Jiang, X. Xiang, and N. Li, Study on weights sensitivity of multi objective decision-making, Journal of China Three Gorges University(Natural Sciences), vol. 26, no. 5, pp. 447–449, 2004.
J. Yu, Y. Chen, J. Liu, J. Wu, J. Fu, and H. Xu, One-at-a-time based weight sensitivity analysis in spatial multi-criteria decision making, Resources Science, vol. 36, no. 9, pp. 1870–1879, 2014.
N. Zhang, R. Chen, and S. Guo, State-of-the-art and future of voting theory, Computer Science, vol. 42, no. 5, pp. 1–9, 23, 2015.
This work was supported by the National Key R&D Program of China (No. 2017YFB1400105).
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