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Bottom-up approaches to achieve Pareto optimal agreements in group decision making

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Abstract

In this article, we introduce a new paradigm to achieve Pareto optimality in group decision-making processes: bottom-up approaches to Pareto optimality. It is based on the idea that, while resolving a conflict in a group, individuals may trust some members more than others; thus, they may be willing to cooperate and share more information with those members. Therefore, one can divide the group into subgroups where more cooperative mechanisms can be formed to reach Pareto optimal outcomes. This is the first work that studies such use of a bottom-up approach to achieve Pareto optimality in conflict resolution in groups. First, we prove that an outcome that is Pareto optimal for subgroups is also Pareto optimal for the group as a whole. Then, we empirically analyze the appropriate conditions and achievable performance when applying bottom-up approaches under a wide variety of scenarios based on real-life datasets. The results show that bottom-up approaches are a viable mechanism to achieve Pareto optimality with applications to group decision-making, negotiation teams, and decision making in open environments.

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Notes

  1. This is similar to the classic machine learning notion of cluster, where the space has as many dimensions as outcomes in the domain, and a point represents the evaluation of an agent for each of the outcomes in the domain.

  2. Euclidean distance.

  3. The total number is \(min\left( 1000,\left( {\begin{array}{c}m\\ n\end{array}}\right) \right) \).

  4. The number of Pareto optimal outcomes calculated in the subgroups compared to the total number of Pareto optimal outcomes in the whole group.

  5. Product of utilities of the agents in the group.

  6. Subgroups of size 2 for teams of size 5, subgroups of size 2, and 3 for groups of size 7, and subgroups of size 2, 3, and 4 for subgroups of size 9.

  7. Subgroups of size 3, 4, and 5 for groups of size 5, 7 and 9 respectively.

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Acknowledgements

This work is part of the Veni research programme with project number 639.021.751, which is financed by the Netherlands Organisation for Scientific Research (NWO) and is supported by ITEA M2MGrids Project, Grant Number ITEA141011. We would like to thank the anonymous reviewers for their useful comments.

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Authors and Affiliations

  1. Florida Universitaria, Carrer del Rei en Jaume I, 2, 46470, Catarroja, Spain

    Victor Sanchez-Anguix

  2. Universidad Isabel I, Calle de Fernan Gonzalez, 76, Burgos, Spain

    Victor Sanchez-Anguix

  3. Coventry University, Gulson Rd, Coventry, CV1 2JH, United Kingdom

    Victor Sanchez-Anguix

  4. Department of Computer Science, Ozyegin University, Istanbul, Turkey

    Reyhan Aydoğan

  5. Department of Intelligent Systems, Delft University of Technology, 2628XE, Delft, The Netherlands

    Reyhan Aydoğan & Catholijn Jonker

  6. Centrum Wiskunde & Informatica, Science Park 123, 1098 XG, Amsterdam, The Netherlands

    Tim Baarslag

  7. Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, De Uithof, 3584 CC, Utrecht, The Netherlands

    Tim Baarslag

  8. Leiden University, Snellius building, 2333CA, Leiden, The Netherlands

    Catholijn Jonker

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  1. Victor Sanchez-Anguix
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Correspondence to Victor Sanchez-Anguix.

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Sanchez-Anguix, V., Aydoğan, R., Baarslag, T. et al. Bottom-up approaches to achieve Pareto optimal agreements in group decision making. Knowl Inf Syst 61, 1019–1046 (2019). https://doi.org/10.1007/s10115-018-01325-y

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