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AFRYCA 2.0: an improved analysis framework for consensus reaching processes

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Abstract

Consensus reaching processes (CRPs) are increasingly important in the resolution of group decision-making (GDM) problems. There are many proposals of CRPs models with different characteristics, being difficult either to choose the most adequate for a given GDM problem or for making comparisons among them. For this reason, AFRYCA was proposed as a framework able to carry out comparison analyses and studies of CRPs in GDM problem resolution. This paper presents AFRYCA 2.0 which overcomes some limitations identified in the previous version. This new version incorporates new features for the analysis of CRPs, and increases its functionality, resulting a more powerful framework. Additionally, to show the usefulness and effectiveness of the new functionality of AFRYCA 2.0, an experimental study is carried out.

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Notes

  1. https://www.r-project.org/.

  2. http://sinbad2.ujaen.es/afryca.

References

  1. Kacprzyk, J.: Group decision making with a fuzzy linguistic majority. Fuzzy Sets Syst. 18(2), 105–118 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  2. Lu, J., Zhang, G., Ruan, D., Wu, F.: Multi-Objective Group Decision Making. Imperial College Press (2006)

  3. Herrera, F., Herrera-Viedma, E., Verdegay, J.: A sequential selection process in group decision making with linguistic assessments. Inf. Sci. 85(4), 223–239 (1995)

    Article  MATH  Google Scholar 

  4. Roubens, M.: Fuzzy sets and decision analysis. Fuzzy Sets Syst. 90(2), 199–206 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  5. Saint, S., Lawson, J.R.: Rules for Reaching Consensus. A Modern Approach to Decision Making. Jossey-Bass (1994)

  6. Herrera-Viedma, E., Cabrerizo, F., Kacprzyk, J., Pedrycz, W.: A review of soft consensus models in a fuzzy environment. Inf. Fusion 17, 4–13 (2014)

    Article  Google Scholar 

  7. Palomares, I., Estrella, F., Martínez, L., Herrera, F.: Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study. Inf. Fusion 20(2014), 252–271 (2014)

    Article  Google Scholar 

  8. Orlovsky, S.: Decision-making with a fuzzy preference relation. Fuzzy Sets Syst. 1(3), 155–167 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  9. Butler, C., Rothstein, A.: On Conflict and Consensus: A Handbook on Formal Consensus Decision Making. Food Not Bombs Publishing (2006)

  10. Elzinga, C., Wang, H., Lin, Z., Kumar, Y.: Concordance and consensus. Inf. Sci. 181(12), 2529–2549 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  11. Martínez, L., Montero, J.: Challenges for improving consensus reaching process in collective decisions. New Math. Nat. Comput. 3(2), 203–217 (2007)

    Article  MATH  Google Scholar 

  12. Bryson, N.: Group decision-making and the analytic hierarchy process. exploring the consensus-relevant information content. Comput. Oper. Res. 23(1), 27–35 (1996)

    Article  MATH  Google Scholar 

  13. Carlsson, C., Ehrenberg, D., Eklund, P., Fedrizzi, M., Gustafsson, P., Lindholm, P., Merkuryeva, G., Riissanen, T., Ventre, A.: Consensus in distributed soft environments. Eur. J. Oper. Res. 61(1–2), 165–185 (1992)

    Article  Google Scholar 

  14. Herrera-Viedma, E., Herrera, F., Chiclana, F.: A consensus model for multiperson decision making with different preference structures. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 32(3), 394–402 (2002)

    Article  MATH  Google Scholar 

  15. Mata, F., Martínez, L., Herrera-Viedma, E.: An adaptive consensus support model for group decision-making problems in a multigranular fuzzy linguistic context. IEEE Trans. Fuzzy Syst. 17(2), 279–290 (2009)

    Article  Google Scholar 

  16. Ben-Arieh, D., Chen, Z.: Linguistic labels aggregation and consensus measure for autocratic decision-making using group recommendations. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 36(1), 558–568 (2006)

    Article  Google Scholar 

  17. Wu, Z., Xu, J.: A consistency and consensus based decision support model for group decision making with multiplicative preference relations. Decis. Support Syst. 52(3), 757–767 (2012)

    Article  Google Scholar 

  18. Zhang, G., Dong, Y., Xu, Y., Li, H.: Minimum-cost consensus models under aggregation operators. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 41(6), 1253–1261 (2011)

    Article  Google Scholar 

  19. Eklund, P., Rusinowska, A., De Swart, H.: A consensus model of political decision-making. Ann. Oper. Res. 158(1), 5–20 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  20. Fu, C., Yang, S.: An evidential reasoning based consensus model for multiple attribute group decision analysis problems with interval-valued group consensus requirements. Eur. J. Oper. Res. 223(1), 167–176 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  21. Gong, Z., Forrest, J., Yang, Y.: The optimal group consensus models for 2-tuple linguistic preference relations. Knowl. Based Syst. 37, 427–437 (2013)

    Article  Google Scholar 

  22. Kacprzyk, J., Zadrozny, S.: Soft computing and web intelligence for supporting consensus reaching. Soft Comput. 14(8), 833–846 (2010)

    Article  Google Scholar 

  23. Palomares, I., Sánchez, P., Quesada, F., Mata, F., Martínez, L.: COMAS: A multi-agent system for performing consensus processes. In: A. Abraham et al. (Eds.) International Symposium on Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol. 91, pp. 125–132. Springer (2011)

  24. Parreiras, R., Ekel, P., Martini, J., Palhares, R.: A flexible consensus scheme for multicriteria group decision making under linguistic assessments. Inf. Sci. 180(7), 1075–1089 (2010)

    Article  Google Scholar 

  25. Wu, Z., Xu, J.: Consensus reaching models of linguistic preference relations based on distance functions. Soft Comput. 16(4), 577–589 (2012)

    Article  MATH  Google Scholar 

  26. Eclipse Foundation: Eclipse RCP: Eclipse Rich Client Platform (Version 4.6). https://wiki.eclipse.org/Rich_Client_Platform (2016)

  27. Chiclana, F., Herrera-Viedma, E., Alonso, S., Herrera, F.: Cardinal consistency of reciprocal preference relations: a characterization of multiplicative transitivity. IEEE Trans. Fuzzy Syst. 17(1), 14–23 (2009)

    Article  Google Scholar 

  28. Palomares, I., Martínez, L., Herrera, F.: MENTOR: A graphical monitoring tool of preferences evolution in large-scale group decision making. Knowl. Based Syst. 58, 66–74 (2014)

    Article  Google Scholar 

  29. Chiclana, F., Mata, F., Martínez, L., Herrera-Viedma, E., Alonso, S.: Integration of a consistency control module within a consensus model. Int. J. Uncertain. Fuzziness Knowl Based Syst. 16(1), 35–53 (2008)

    Article  MathSciNet  Google Scholar 

  30. Kacprzyk, J., Zadrozny, S.: Supporting consensus reaching processes under fuzzy preferences and a fuzzy majority via linguistic summaries. In: Greco, S., Pereira, R.A.M., Squillante, M., Yager, R.R., Kacprzyk, J. (eds.) Preferences and Decisions. Studies in fuzziness and soft computing, vol. 257, pp. 261–279. Springer, Berlin (2010)

  31. Palomares, I., Martínez, L.: A semi-supervised multi-agent system model to support consensus reaching processes. IEEE Trans. Fuzzy Syst. 22(4), 762–777 (2014)

    Article  Google Scholar 

  32. Wu, Z., Xu, J.: A concise consensus support model for group decision making with reciprocal preference relations based on deviation measures. Fuzzy Sets Syst. 206(1), 58–73 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  33. Xu, Y., Li, K., Wang, H.: Distance-based consensus models for fuzzy and multiplicative preference relations. Inf. Sci. 253, 56–73 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  34. Algorithmics Group: MDSJ: Java Library for Multidimensional Scaling (Version 0.2). http://www.inf.uni-konstanz.de/algo/software/mdsj/ (2009)

  35. The Apache Software Foundation: Commons Math: The Apache Commons Mathematics Library (Version 3.6.1). https://commons.apache.org/math/ (2016)

  36. Palomares, I., Quesada, F., Martínez, L.: An approach based on computing with words to manage experts’ behavior in consensus reaching processes with large groups. In: Proceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2014) pp. 476–483 (2014)

  37. Quesada, F., Palomares, I., Martínez, L.: Managing experts behavior in large-scale consensus reaching processes with uninorm aggregation operators. Appl. Soft Comput. 35, 873–887 (2015)

    Article  Google Scholar 

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Acknowledgements

This paper is partially funded by the research project TIN2015-66524-P.

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Correspondence to Álvaro Labella.

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Labella, Á., Estrella, F.J. & Martínez, L. AFRYCA 2.0: an improved analysis framework for consensus reaching processes. Prog Artif Intell 6, 181–194 (2017). https://doi.org/10.1007/s13748-016-0108-y

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