Agent-Based Modelling of Social Emotional Decision Making in Emergency Situations

  • Tibor BosseEmail author
  • Mark Hoogendoorn
  • Michel Klein
  • Alexei SharpanskykhEmail author
  • Jan Treur
  • C. Natalie van der Wal
  • Arlette van Wissen
Part of the Understanding Complex Systems book series (UCS)


Social decision making under stressful circumstances may involve strong emotions and contagion from others. Recent developments in Social Neuroscience have revealed neural mechanisms by which social contagion of cognitive and emotional states can be realised. In this paper, based on these mechanisms, an agent-based computational model is proposed. Furthermore, it is demonstrated how the proposed cognitive model can be transformed into an equivalent behavioural model without any cognitive states. As an application of the model, a computational analysis was performed of patterns in crowd behaviour, in particular by agent-based simulation of a real-life incident that took place on May 4, 2010 in Amsterdam. The results of the model analysis show the inclusion of contagion of belief, emotion, and intention states of agents results in better reproduction of the incident than non-inclusion.


Body State Agent Model Mirror Neuron Combination Function Sensory Representation 
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.


  1. 1.
    Ashby, W.R.: Design for a Brain. Chapman and Hall, London (1952)Google Scholar
  2. 2.
    Bechara, A., Damasio, A.: The somatic marker hypothesis: a neural theory of economic decision. Games Econ. Behav. 52, 336–372 (2005)zbMATHCrossRefGoogle Scholar
  3. 3.
    Becker, W., Fuchs, A.F.: Prediction in the oculomotor system: smooth pursuit during transient disappearance of a visual target. Exp. Brain Res. 57, 562–575 (1985)CrossRefGoogle Scholar
  4. 4.
    Beer, R.D.: On the dynamics of small continuous-time recurrent neural networks. Adapt. Behav. 3, 469–509 (1995)CrossRefGoogle Scholar
  5. 5.
    Bosse, T., Memon, Z.A., Treur, J., Umair, M., An adaptive human-aware software agent supporting attention-demanding tasks. In: Yang, J.-J., Yokoo, M., Ito, T., Jin, Z., Scerri, P. (eds.) Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems, PRIMA’09, Lecture Notes in AI, vol. 5925, pp. 292–307. Springer Verlag, Heidelberg (2009)Google Scholar
  6. 6.
    Braun, A., Musse, S.R., de Oliveira, L.P.L., Bodmann, B.E.J.: Modeling individual behaviors in crowd simulation. In: the 16th International Conference on Computer Animation and Social Agents CASA 2003, pp.143–147. IEEE Press, New Jersey (2003)Google Scholar
  7. 7.
    Côté, S.: Reconciling the feelings-as-information and hedonic contingency models of how mood influences systematic information processing. J. Appl. Soc. Psychol. 35, 1656–1679 (2005)CrossRefGoogle Scholar
  8. 8.
    Damasio, A.: Descartes’ Error: Emotion, Reason and the Human Brain. Papermac, London (1994)Google Scholar
  9. 9.
    Damasio, A.: The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philos. Trans. Roy. Soc. Biol. Sci. 351, 1413–1420 (1996)CrossRefGoogle Scholar
  10. 10.
    Damasio, A.: The Feeling of What Happens. Body and Emotion in the Making of Consciousness. Harcourt Brace, New York (1999)Google Scholar
  11. 11.
    Damasio, A.: Looking for Spinoza: Joy, Sorrow, and the Feeling Brain. Vintage books, London (2003)Google Scholar
  12. 12.
    Damasio, A.R.: Self Comes to Mind: Constructing the Conscious Brain. Pantheon Books, New York (2010)Google Scholar
  13. 13.
    Decety, J., Cacioppo, J.T. (eds.) The Handbook of Social Neuroscience. Oxford University Press, New York (2010)Google Scholar
  14. 14.
    Frederickson, B.L., Branigan, C.: Positive emotions broaden the scope of attention and thought-action repertoires. Cogn. Emot. 19, 313–332 (2005)CrossRefGoogle Scholar
  15. 15.
    Fried, I., Mukamel, R., Kreiman, G.: Internally generated preactivation of single neurons in human medial frontal cortex predicts volition. Neuron 69(548–562), 2011 (2011)Google Scholar
  16. 16.
    Funahashi, K., Nakamura, Y.: Approximation of dynamical systems by continuous time recurrent neural networks. Neural Netw. 6, 801–806 (1993)CrossRefGoogle Scholar
  17. 17.
    Goldman, A.I.: Simulating Minds: The Philosophy, Psychology, and Neuroscience of Mindreading. Oxford University Press, New York (2006)Google Scholar
  18. 18.
    Grossberg, S.: On learning and energy-entropy dependence in recurrent and nonrecurrent signed networks. J. Stat. Phys. 1, 319–350 (1969)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Guten, S., Allen, V.L.: Likelihood of escape, likelihood of danger, and panic behavior. J. Soc. Psychol. 87, 29–36 (1972)CrossRefGoogle Scholar
  20. 20.
    Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407(6803), 487–490 (2000)CrossRefGoogle Scholar
  21. 21.
    Hodges, W.: Model Theory. Cambridge University Press, Cambridge (1993)zbMATHCrossRefGoogle Scholar
  22. 22.
    Hoogendoorn, M., Treur, J., Wal, C.N. van der, Wissen, A. van.: Modelling the interplay of emotions, beliefs and intentions within collective decision making based on insights from social neuroscience. In: Proceedings of the 17th International Conference on Neural Information Processing, ICONIP’10. Lecture Notes in Artificial Intelligence, pp. 196–206. Springer Verlag, Berlin, Heidelberg (2010)Google Scholar
  23. 23.
    Hopfield, J.J.: Neural networks and physical systems with emergent collective computational properties. Proc. Nat. Acad. Sci. (USA) 79, 2554–2558 (1982)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Hopfield, J.J.: Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Nat. Acad. Sci. (USA) 81, 3088–3092 (1984)CrossRefGoogle Scholar
  25. 25.
    Iacoboni, M.: Mirroring People: The New Science of How We Connect with Others. Farrar, Straus & Giroux, New York (2008)Google Scholar
  26. 26.
    James, W.: What is an emotion. Mind 9, 188–205 (1884)CrossRefGoogle Scholar
  27. 27.
    Keysers, C., Gazzola, V.: Social neuroscience: mirror neurons recorded in humans. Curr. Biol. 20, 253–254 (2010)CrossRefGoogle Scholar
  28. 28.
    Morrison, S.E., Salzman, C.D.: Re-valuing the amygdala. Curr. Opin. Neurobiol. 20, 221–230 (2010)CrossRefGoogle Scholar
  29. 29.
    Mukamel, R., Ekstrom, A.D., Kaplan, J., Iacoboni, M., Fried, I.: Single-neuron responses in humans during execution and observation of actions. Curr. Biol. 20, 750–756 (2010)CrossRefGoogle Scholar
  30. 30.
    Murray, E.A.: The amygdala, reward and emotion. Trends Cogn. Sci. 11, 489–497 (2007)CrossRefGoogle Scholar
  31. 31.
    Musse, S.R., Thalmann, D.: A model of human crowd behavior: group inter-relationship and collision detection analysis. Comput. Animat. Simulat. 97, 39–51 (1997)Google Scholar
  32. 32.
    Pan, X., Han, C., Dauber, K., Law, K.: Human and social behaviour in computational modeling and analysis of egress. Automat. Constr. 15, 448–461 (2006)CrossRefGoogle Scholar
  33. 33.
    Pelechano, N., O’brien, K., Silverman, B., Badler, N.: Crowd simulation incorporating agent psychological models, roles and communication. In: First International Workshop on Crowd Simulation, V-CROWDS’05, pp. 21–30. Lausanne (2005)Google Scholar
  34. 34.
    Pineda, J.A. (ed.): Mirror Neuron Systems: The Role of Mirroring Processes in Social Cognition. Humana, New York (2009)Google Scholar
  35. 35.
    Port, R.F., van Gelder, T.: Mind as Motion: Explorations in the Dynamics of Cognition. MIT Press, Cambridge (1995)Google Scholar
  36. 36.
    Rizzolatti, G., Sinigaglia, C.: Mirrors in the Brain: How Our Minds Share Actions and Emotions. Oxford University Press, Oxford (2008)Google Scholar
  37. 37.
    Sakuma, T., Mukai, T., Kuriyama, S.: Psychological model for animating crowded pedestrians. Comput. Animat. Virt. World. 16, 343–351 (2005)CrossRefGoogle Scholar
  38. 38.
    Sharpanskykh, A., Treur, J.: Relating cognitive process models to behavioural models of agents. In: Jain, L., et al. (ed.) Proceeding of the 8th International Conference on Intelligent Agent Technology, IAT’08, pp. 330–335. IEEE Computer Society Press, Sydney, Australia (2008)Google Scholar
  39. 39.
    Sorenson, H.W.: Parameter Estimation: Principles and Problems. Marcel Dekker, New York (1980)zbMATHGoogle Scholar
  40. 40.
    Treur, J.: On the use of reduction relations to relate different types of agent models. Web Intell. Agent Syst., to appear 9(1), 81–95 (2011)Google Scholar
  41. 41.
    Ulicny, B., Thalmann, D.: Crowd simulation for interactive virtual environments and VR training systems. In: Proceedings of the Eurographics Workshop on Animation and Simulation’01, pp. 163–170. Springer-Verlag, Heidelberg (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tibor Bosse
    • 1
    Email author
  • Mark Hoogendoorn
    • 1
  • Michel Klein
    • 1
  • Alexei Sharpanskykh
    • 2
    Email author
  • Jan Treur
    • 1
  • C. Natalie van der Wal
    • 1
  • Arlette van Wissen
    • 1
  1. 1.VU University AmsterdamAmsterdamThe Netherlands
  2. 2.Delft University of TechnologyDelftThe Netherlands

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