Swarm Intelligence

, Volume 9, Issue 2–3, pp 75–102 | Cite as

A quantitative micro–macro link for collective decisions: the shortest path discovery/selection example

  • Andreagiovanni Reina
  • Roman Miletitch
  • Marco Dorigo
  • Vito Trianni


In this paper, we study how to obtain a quantitative correspondence between the dynamics of the microscopic implementation of a robot swarm and the dynamics of a macroscopic model of nest-site selection in honeybees. We do so by considering a collective decision-making case study: the shortest path discovery/selection problem. In this case study, obtaining a quantitative correspondence between the microscopic and macroscopic dynamics—the so-called micro–macro link problem—is particularly challenging because the macroscopic model does not take into account the spatial factors inherent to the path discovery/selection problem. We frame this study in the context of a general engineering methodology that prescribes the inclusion of available theoretical knowledge about target macroscopic models into design patterns for the microscopic implementation. The attainment of the micro–macro link presented in this paper represents a necessary step towards the formalisation of a design pattern for collective decision making in distributed systems.


Collective decision making Micro–macro link Shortest path selection Swarm robotics Design pattern 



This work was partially supported by the European Research Council through the ERC Advanced Grant “E-SWARM: Engineering Swarm Intelligence Systems” (contract 246939). Vito Trianni acknowledges support from the EU-FP7 Project “DICE: Distributed Cognition Engineering” funded by the European Commissions FP7 People Programme under the Marie Curie Career Integration Grant scheme (Project ID: 631297). Marco Dorigo acknowledges support from the Belgian F.R.S.-FNRS. We thank Gabriele Valentini for sharing the code of the Gillespie algorithm used for the study of macroscopic finite-size effects and Carlo Pinciroli for the support in the implementation of the swarm robotics simulations.

Supplementary material

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Andreagiovanni Reina
    • 1
  • Roman Miletitch
    • 1
  • Marco Dorigo
    • 1
  • Vito Trianni
    • 2
  1. 1.IRIDIAUniversité Libre de BruxellesBrusselsBelgium
  2. 2.ISTCItalian National Research CouncilRomeItaly

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