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Scaling Things Up: Large Group Decision Making (LGDM)

  • Iván Palomares Carrascosa
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

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.

References

  1. 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.Google Scholar
  2. 3.
    Arrow, K.J.: A difficulty in the concept of social welfare. Journal of Political Economy, 58(4), pp. 328–346, 1950.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. 6.
    Baker, K.R.: Management Science: An Introduction to the Use of Decision Models. Wiley (NY), 1985.Google Scholar
  5. 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.CrossRefGoogle Scholar
  6. 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.Google Scholar
  7. 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.CrossRefGoogle Scholar
  8. 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.Google Scholar
  9. 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.Google Scholar
  10. 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.Google Scholar
  11. 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.CrossRefGoogle Scholar
  12. 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.Google Scholar
  13. 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.CrossRefGoogle Scholar
  14. 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.CrossRefGoogle Scholar
  15. 43.
    Felfernig, A., Boratto, L., Stettinger, M., Tkalcic, M.: Group Recommender Systems - an Introduction. SpringerBriefs in Electrical and Computer Engineering, Springer, 2018.CrossRefGoogle Scholar
  16. 44.
    Flach, P.: Machine Learning: The Art and Science of Algorithms that make sense of Data. Cambridge University Press, 2012.CrossRefGoogle Scholar
  17. 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.MathSciNetCrossRefGoogle Scholar
  18. 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.Google Scholar
  19. 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.CrossRefGoogle Scholar
  20. 64.
    Husain, A.J.A.: A multi-agent system for scalable group decision making.Google Scholar
  21. 71.
    Kacprzyk, J., Zadrozny, S.: Soft computing and web intelligence for supporting consensus reaching. Soft Computing, 14(8), pp. 833–846, 2010.CrossRefGoogle Scholar
  22. 77.
    Lawry, J., Tang, Y.: Uncertainty modelling for vague concepts: A prototype theory approach. Artificial Intelligence, 173(18), pp. 1539–1558, 2009.MathSciNetCrossRefGoogle Scholar
  23. 80.
    Liu, H.C., You, X.Y., Tsung, F., Ji, P.: An improved approach for failure mode and effect analysis involving large group of experts: an application to the healthcare field. Quality Engineering, In press. https://doi.org/10.1080/08982112.2018.1448089.
  24. 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.CrossRefGoogle Scholar
  25. 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.MathSciNetCrossRefGoogle Scholar
  26. 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.Google Scholar
  27. 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.CrossRefGoogle Scholar
  28. 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.CrossRefGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. 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.CrossRefGoogle Scholar
  31. 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.Google Scholar
  32. 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.Google Scholar
  33. 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.Google Scholar
  34. 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.Google Scholar
  35. 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.Google Scholar
  36. 119.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach (3rd Ed.). Pearson, 2016.Google Scholar
  37. 128.
    Squillante, M.: Decision making in social networks. International Journal of Intelligent Systems, 25(3), Special Issue, 2010.Google Scholar
  38. 130.
    Shi, Z.J., Wang, X.Q., Palomares, I., Guo, S.J., Ding, R.X.: A novel consensus model for multi-attribute large-scale group decision making based on comprehensive behavior Classification and adaptive weight updating. Knowledge-based Systems, In Press. https://doi.org/10.1016/j.knosys.2018.06.002 CrossRefGoogle Scholar
  39. 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.CrossRefGoogle Scholar
  40. 132.
    Smith, J.E., Winterfeldt, D.: Decision Analysis in “Management Science”. Management Science, 50(5), pp. 561–574, 2004.CrossRefGoogle Scholar
  41. 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.CrossRefGoogle Scholar
  42. 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.CrossRefGoogle Scholar
  43. 148.
    von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press (NJ), 1944.Google Scholar
  44. 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.CrossRefGoogle Scholar
  45. 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.CrossRefGoogle Scholar
  46. 160.
    Xu, Z.: An automatic approach to reaching consensus in multiple attribute group decision making. Computers & Industrial Engineering, 56(4), pp. 1369–1374, 2009.CrossRefGoogle Scholar
  47. 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.CrossRefGoogle Scholar
  48. 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.CrossRefGoogle Scholar
  49. 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.CrossRefGoogle Scholar
  50. 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.CrossRefGoogle Scholar
  51. 192.
    Zhang, H., Palomares, I., Dong, Y., Wang, W.: Managing non-cooperative behaviors in consensus-based multiple attribute group decision making: An approach based on social network analysis. Knowledge-based Systems, In press. https://doi.org/10.1016/j.knosys.2018.06.008 CrossRefGoogle Scholar
  52. 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.CrossRefGoogle Scholar
  53. 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.CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Iván Palomares Carrascosa
    • 1
  1. 1.School of Computer Science (SCEEM)University of BristolBristolUK

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