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

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. 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. 2.

    Euclidean distance.

  3. 3.

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

  4. 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. 5.

    Product of utilities of the agents in the group.

  6. 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. 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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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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Keywords

  • Agreement technologies
  • Automated negotiation
  • Pareto optimality
  • Group decision making
  • Multi-agent systems