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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

3. Scaling Things Up: Large Group Decision Making (LGDM)

verfasst von : Iván Palomares Carrascosa

Erschienen in: Large Group Decision Making

Verlag: Springer International Publishing

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Abstract

What is a Large Group Decision Making problem? What differentiates them from the conventional Group Decision Making problems and approaches introduced in the previous chapter, and what are the added complexities of supporting high-quality decisions to be made by large groups? The present chapter aims at introducing and contextualizing this relatively new area of research, highlighting its main limitations of challenges and discussing some of its newly related disciplines, as witnessed in recent research.

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Vorheriges Kapitel Group Decision Making and Consensual Processes
Nächstes Kapitel LGDM Approaches and Models: A Literature Review
Literatur
1.
Alcantud, J.C.R., de Andrés, R.: A fuzzy viewpoint of consensus measures in social choice. ESTYLF 2014 Proceedings: XVII Spanish Conference on Fuzzy Logic and Technologies, pp. 87–92, 2014.
3.
Arrow, K.J.: A difficulty in the concept of social welfare. Journal of Political Economy, 58(4), pp. 328–346, 1950.CrossRef
5.
B’́ack, E., Esaiasson, P., Gilljam, M., Svenson, O., Lindholm, T.: Post-Decision Consolidation in Large Group decision-making. Cognition and Neurosciences. Scandinavian Journal of Psychology, 52, pp. 320–328, 2011.CrossRef
6.
Baker, K.R.: Management Science: An Introduction to the Use of Decision Models. Wiley (NY), 1985.
15.
Bryson, N.: Group decision-making and the analytic hierarchy process. exploring the consensus-relevant information content. Computers and Operations Research, 23(1), pp. 27–35, 1996.CrossRef
17.
Bullock, S., Crowder, R., Pitonakova, L.: Task allocation in foraging robot swarms: The role of information sharing. Proceedings of the European Conference on Artificial Life 13, pp. 306–313, 2016.
22.
Carneiro, J., Saraiva, P., Martinho, D., Marreiros, G., Novais, P.: Representing decision-makers using styles of behavior: an approach designed for group decision support systems. Cognitive Systems Research, 47, pp. 109–132, 2018.CrossRef
23.
Cartlidge, J., Cliff, D.: Modelling complex financial markets using real-time human-agent trading experiments. In Chen S.H. et al. (Eds.): Complex Systems Modeling and Simulation in Economics and Finance, Springer, 2018.
24.
Carvalho, G., Vivacqua, A.S., Souza, J.M., Medeiros, S.P.J.: LaSca: a Large Scale Group Decision Support System. Proceedings of 12th International Conference on Computer Supported Cooperative Work in Design. Xi’an (China), 2008.
25.
Chadwick, A.: Web 2.0: New challenges for the study of e-democracy in an era of informational exuberance. I/S: A Journal of Law and Policy for the Information Society, 5(1), pp. 9–41, 2009.
29.
Chin, K.S., Xu, D.L., Yang, J.B., Lam, J.P.-K.: Group-based ER-AHP system for product project screening. Expert Systems with Applications, 35(4), pp. 1909–1929, 2008.CrossRef
33.
Crosscombe, M., Lawry, J.: Exploiting vagueness for multi-agent consensus. Multi-agent and Complex Systems, Studies in Computational Intelligence, vol. 670, pp. 67–78, Springer, 2017.
36.
Dong, Y., Zhang, H., Herrera-Viedma, E.: Integrating experts’ weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors. Decision Support Systems, 84, pp. 1–15, 2016.CrossRef
40.
Dong, Y., Zhan, M., Kou, G., Ding, Z., Liang, H.: A survey of the fusion process in opinion dynamics. Information Fusion, 43, pp. 57–65, 2018.CrossRef
43.
Felfernig, A., Boratto, L., Stettinger, M., Tkalcic, M.: Group Recommender Systems - an Introduction. SpringerBriefs in Electrical and Computer Engineering, Springer, 2018.CrossRef
44.
Flach, P.: Machine Learning: The Art and Science of Algorithms that make sense of Data. Cambridge University Press, 2012.CrossRef
46.
García-Lapresta, J.L., Llamazares, B.: Aggregation of fuzzy preferences: some rules of the mean. Social Choice and Welfare, 17(4), pp. 673–690.MathSciNetCrossRef
48.
Goel, A., Lee, D.T.: Towards large-scale deliberative decision-making: small groups and the importance of triads. EC ’16 Proceedings of the 2016 ACM Conference on Economics and Computation, pp. 287–303, 2016.
63.
Hoegen, A., Steininger, D., Veit, D.: How do investors decide? An interdisciplinary review of decision-making in crowdfunding. Electronic Markets, Oct. 2017, pp. 1–27, 2017.CrossRef
64.
Husain, A.J.A.: A multi-agent system for scalable group decision making.
71.
Kacprzyk, J., Zadrozny, S.: Soft computing and web intelligence for supporting consensus reaching. Soft Computing, 14(8), pp. 833–846, 2010.CrossRef
77.
Lawry, J., Tang, Y.: Uncertainty modelling for vague concepts: A prototype theory approach. Artificial Intelligence, 173(18), pp. 1539–1558, 2009.MathSciNetCrossRef
80.
84.
Liu, B., Chen, Y., Shen, Y., Sun, H., Xu, X.: A complex multi-attribute large-group decision making method based on the interval-valued intuitionistic fuzzy principal component analysis model. Soft Computing, 18, pp. 2149–2160, 2014.CrossRef
85.
Liu, B., Shen, Y., Zhang, W., Chen, X., Wang, X.: An interval-valued intuitionistic fuzzy principal component analysis model-based method for complex multi-attribute large-group decision-making. European Journal of Operational Research, 245, pp. 209–225, 2015.MathSciNetCrossRef
100.
Palomares, I., Sánchez, P., Quesada, F., Mata, F., Martínez, L.: COMAS - A multi-agent system for performing consensus processes. In Abraham, A. et al. (Eds.): Procs. International Symposium on Distributed Computing and Artificial Intelligence (DCAI 2011). Advances in Intelligent and Soft Computing, 91, pp. 125–132, Springer, 2011.
103.
Palomares, I., Estrella, F.J., Martinez, L., Herrera, F.: Consensus under a fuzzy context - taxonomy, analysis framework AFRYCA and experimental case of study. Information fusion, 20, pp. 252–271, 2014.CrossRef
104.
Palomares, I., Martinez, L.: A semisupervised multiagent system model to support consensus-reaching processes. IEEE Transactions on Fuzzy Systems, 22(4), pp. 762–777, 2014.CrossRef
105.
Palomares, I., Martínez, L., Herrera, F.: A consensus model to detect and manage non-cooperative behaviors in large-scale group decision making. IEEE Transactions on Fuzzy Systems, 22(3), pp. 516–530, 2014.CrossRef
107.
Palomares, I., Martínez, L., Herrera, F.: MENTOR: A graphical monitoring tool of preferences evolution in large-scale group decision making. Knowledge-based Systems, 58 (Spec.Iss.), pp. 66–74, 2014.CrossRef
108.
Palomares, I.: Multi-agent System to model consensus processes in large-scale group decision making using soft computing techniques. PhD Thesis, University of Jaén (Spain), 2014.
110.
Palomares, I., Killough, R., Bauters, K., Liu, W., Hong, J.: A collaborative multiagent framework based on online risk-aware planning and decision-making. In Proceedings of ICTAI’16 International Conference, 2016.
111.
Palomares, I., Sellak, H., Ouhbi, B., Frikh, B.: Adaptive, Semi-Supervised Consensus Model for Multi-Criteria Large Group Decision Making in a Linguistic Setting. In ISKE 2017 Proceedings: 12th International Conference on Intelligent Systems and Knowledge Engineering, 2017.
116.
Rodríguez, M.A.: Advances towards a general-purpose societal-scale human-collective problem-solving engine. Procs. 2004 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 206–211, 2004.
117.
Rodriguez, M.A.: Social decision making with multi-relational networks and grammar-based particle swarms. Procs. 40th Hawaii International Conference on System Sciences, 2007.
119.
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach (3rd Ed.). Pearson, 2016.
128.
Squillante, M.: Decision making in social networks. International Journal of Intelligent Systems, 25(3), Special Issue, 2010.
130.
131.
Shum, S., Cannavacciuolo, L., De Liddo, A., Iandoli, L., Quinto, I.: Using social network analysis to support collective decision-making processes. International Journal of Decision Support System Technology, 3(2), pp. 15–31, 2011.CrossRef
132.
Smith, J.E., Winterfeldt, D.: Decision Analysis in “Management Science”. Management Science, 50(5), pp. 561–574, 2004.CrossRef
134.
Soto, R., Robles-Baldenegro, M.E., López, V.: MQDM: An iterative fuzzy method for group decision making in structured social networks. International Journal of Intelligent Systems, 32, pp. 17–30, 2017.CrossRef
135.
Srdjevic, B.: Linking analytic hierarchy process and social choice methods to support group decision-making in water management. Decision Support Systems, 42, pp. 2261–2273, 2007.CrossRef
148.
von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press (NJ), 1944.
152.
Wu, T., Liu, X.W.: An interval type-2 fuzzy clustering solution for large-scale multiple-criteria group decision-making problems. Knowledge-based Systems, 144, pp. 118–127, 2016.CrossRef
153.
Wu, T., Liu, X., Qin, J.: A linguistic solution for double large-scale group decision-making in E-commerce. Computers & Industrial Engineering, 116, pp. 97–112, 2018.CrossRef
160.
Xu, Z.: An automatic approach to reaching consensus in multiple attribute group decision making. Computers & Industrial Engineering, 56(4), pp. 1369–1374, 2009.CrossRef
163.
Xu, X.H., Cai, C., Chen, X., Zhou, Y.: A multi-attribute large group emergency decision making method based on group preference consistency of generalized interval-valued trapezoidal fuzzy numbers. Journal of Systems Science and Systems Engineering, 24(2), pp. 211–228, 2015.CrossRef
165.
Xu, X.H., Du, Z.J., Chen, X.H.: Consensus model for multi-criteria large-group emergency decision making considering non-cooperative behaviors and minority opinions. Decision Support Systems, 79, pp. 150–160, 2015.CrossRef
174.
Yang, Y., Fu, C., Chen, Y.-W., Xu, D.-L., Yang, S.-L.: A belief rule based expert system for predicting consumer preference in new product development. Knowledge-based Systems, 94, pp. 105–113, 2016.CrossRef
189.
Zhang, Z., Guo, C., Martínez, L.: Managing multigranular linguistic distribution assessments in large-scale multiattribute group decision making. IEEE Transactions on Systems, Man and Cybernetics: Systems, 47(11), pp. 3063–3076, 2017.CrossRef
192.
193.
Zhu, W.D., Liu, F., Chen, Y.W., Yang, J.B., Xu, D.L., Wang, D.P.: Research project evaluation and selection: an evidential reasoning rule-based method for aggregating peer review information with reliabilities. Scientometrics, 105(3), pp. 1469–1490, 2015.CrossRef
194.
Zhu, J., Zhang, S., Chen, Y., Zhang, L.: A Hierarchical Clustering Approach Based on Three-Dimensional Gray Relational Analysis for Clustering a Large Group of Decision Makers with Double Information. Group Decision and Negotiation, 25, pp. 325–354, 2016.CrossRef
Metadaten
Titel
Scaling Things Up: Large Group Decision Making (LGDM)
verfasst von
Iván Palomares Carrascosa
Copyright-Jahr
2018
Buch
Large Group Decision Making

Print ISBN: 978-3-030-01026-3

Electronic ISBN: 978-3-030-01027-0

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01027-0_3