Using Graph Properties and Clustering Techniques to Select Division Mechanisms for Scalable Negotiations

  • Ivan Marsa-MaestreEmail author
  • Catholijn M. Jonker
  • Mark Klein
  • Enrique de la Hoz
Part of the Studies in Computational Intelligence book series (SCI, volume 674)


This paper focuses on enabling the use of negotiation for complex system optimisation, which main challenge nowadays is scalability. Our hypothesis is that analysing the underlying network structure of these systems can help divide the problems in subproblems which facilitate distributed decision making through negotiation in these domains. In this paper, we verify this hypothesis with an extensive set of scenarios for a proof-of-concept problem. After selecting a set of network metrics for analysis, we cluster the scenarios according to these metrics and evaluate a set of mediation mechanisms in each cluster. The validation experiments show that the relative performance of the different mediation mechanisms change for each cluster, which confirms that network-based metrics may be useful for mechanism selection in complex networks.


Cluster Coefficient Betweenness Centrality Evacuation Time Combinatorial Auction Mediation Mechanism 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research has been partially supported by two research grants from the Spanish Ministry of Economy and Competitiveness (grants TEC2013-45183-R and TIN2014-61627-EXP).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ivan Marsa-Maestre
    • 1
    Email author
  • Catholijn M. Jonker
    • 2
  • Mark Klein
    • 3
  • Enrique de la Hoz
    • 1
  1. 1.University of AlcalaMadridSpain
  2. 2.Technical University of DelftDelftThe Netherlands
  3. 3.Massachusetts Institute of TechnologyCambridgeUSA

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