Modelling collective decision making in groups and crowds: Integrating social contagion and interacting emotions, beliefs and intentions

  • Tibor Bosse
  • Mark Hoogendoorn
  • Michel C. A. Klein
  • Jan Treur
  • C. Natalie van der Wal
  • Arlette van Wissen
Open Access
Article

Abstract

Collective decision making involves on the one hand individual mental states such as beliefs, emotions and intentions, and on the other hand interaction with others with possibly different mental states. Achieving a satisfactory common group decision on which all agree requires that such mental states are adapted to each other by social interaction. Recent developments in social neuroscience have revealed neural mechanisms by which such mutual adaptation can be realised. These mechanisms not only enable intentions to converge to an emerging common decision, but at the same time enable to achieve shared underlying individual beliefs and emotions. This paper presents a computational model for such processes. As an application of the model, an agent-based analysis was made of patterns in crowd behaviour, in particular to simulate a real-life incident that took place on May 4, 2010 in Amsterdam. From available video material and witness reports, useful empirical data were extracted. Similar patterns were achieved in simulations, whereby some of the parameters of the model were tuned to the case addressed, and most parameters were assigned default values. The results show the inclusion of contagion of belief, emotion, and intention states of agents results in better reproduction of the incident than non-inclusion.

Keywords

Computational modelling Collective decision making Social neuroscience Mirroring Belief Emotion Intention Crowd behaviour 

Notes

Acknowledgments

This research has partly been conducted as part of the FP7 ICT Future Enabling Technologies program of the European Commission under grant agreement No. 231288 (SOCIONICAL). Furthermore, we would like to thank Milind Tambe and Jason Tsai from the University of Southern California for the fruitful discussions.

Open Access

This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

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Copyright information

© The Author(s) 2012

Authors and Affiliations

  • Tibor Bosse
    • 1
  • Mark Hoogendoorn
    • 1
  • Michel C. A. Klein
    • 1
  • Jan Treur
    • 1
  • C. Natalie van der Wal
    • 1
  • Arlette van Wissen
    • 1
  1. 1.Department of Artificial IntelligenceVU UniversityAmsterdamThe Netherlands

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