Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
A short movie with images from the live broadcast on Dutch National Television, can be found at: http://www.youtube.com/watch?v=0cEQp8OQj2Y. This shows how, within two minutes, the crowd starts to panic and move.
- 2.
Permission granted for educational and research purposes by The Netherlands Institute for Sound and Vision.
- 3.
See http://www.few.vu.nl/~tbosse/may4/. This URL contains two animations: one in which only the result of the model with contagion is shown, and one in which the results of all four models are shown together.
References
Ashby, W.R.: Design for a Brain. Chapman and Hall, London (1952)
Bechara, A., Damasio, A.: The somatic marker hypothesis: a neural theory of economic decision. Games Econ. Behav. 52, 336–372 (2005)
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)
Beer, R.D.: On the dynamics of small continuous-time recurrent neural networks. Adapt. Behav. 3, 469–509 (1995)
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)
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)
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)
Damasio, A.: Descartes’ Error: Emotion, Reason and the Human Brain. Papermac, London (1994)
Damasio, A.: The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philos. Trans. Roy. Soc. Biol. Sci. 351, 1413–1420 (1996)
Damasio, A.: The Feeling of What Happens. Body and Emotion in the Making of Consciousness. Harcourt Brace, New York (1999)
Damasio, A.: Looking for Spinoza: Joy, Sorrow, and the Feeling Brain. Vintage books, London (2003)
Damasio, A.R.: Self Comes to Mind: Constructing the Conscious Brain. Pantheon Books, New York (2010)
Decety, J., Cacioppo, J.T. (eds.) The Handbook of Social Neuroscience. Oxford University Press, New York (2010)
Frederickson, B.L., Branigan, C.: Positive emotions broaden the scope of attention and thought-action repertoires. Cogn. Emot. 19, 313–332 (2005)
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)
Funahashi, K., Nakamura, Y.: Approximation of dynamical systems by continuous time recurrent neural networks. Neural Netw. 6, 801–806 (1993)
Goldman, A.I.: Simulating Minds: The Philosophy, Psychology, and Neuroscience of Mindreading. Oxford University Press, New York (2006)
Grossberg, S.: On learning and energy-entropy dependence in recurrent and nonrecurrent signed networks. J. Stat. Phys. 1, 319–350 (1969)
Guten, S., Allen, V.L.: Likelihood of escape, likelihood of danger, and panic behavior. J. Soc. Psychol. 87, 29–36 (1972)
Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407(6803), 487–490 (2000)
Hodges, W.: Model Theory. Cambridge University Press, Cambridge (1993)
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)
Hopfield, J.J.: Neural networks and physical systems with emergent collective computational properties. Proc. Nat. Acad. Sci. (USA) 79, 2554–2558 (1982)
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)
Iacoboni, M.: Mirroring People: The New Science of How We Connect with Others. Farrar, Straus & Giroux, New York (2008)
James, W.: What is an emotion. Mind 9, 188–205 (1884)
Keysers, C., Gazzola, V.: Social neuroscience: mirror neurons recorded in humans. Curr. Biol. 20, 253–254 (2010)
Morrison, S.E., Salzman, C.D.: Re-valuing the amygdala. Curr. Opin. Neurobiol. 20, 221–230 (2010)
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)
Murray, E.A.: The amygdala, reward and emotion. Trends Cogn. Sci. 11, 489–497 (2007)
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)
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)
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)
Pineda, J.A. (ed.): Mirror Neuron Systems: The Role of Mirroring Processes in Social Cognition. Humana, New York (2009)
Port, R.F., van Gelder, T.: Mind as Motion: Explorations in the Dynamics of Cognition. MIT Press, Cambridge (1995)
Rizzolatti, G., Sinigaglia, C.: Mirrors in the Brain: How Our Minds Share Actions and Emotions. Oxford University Press, Oxford (2008)
Sakuma, T., Mukai, T., Kuriyama, S.: Psychological model for animating crowded pedestrians. Comput. Animat. Virt. World. 16, 343–351 (2005)
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)
Sorenson, H.W.: Parameter Estimation: Principles and Problems. Marcel Dekker, New York (1980)
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)
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)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Bosse, T. et al. (2013). Agent-Based Modelling of Social Emotional Decision Making in Emergency Situations. In: Mitleton-Kelly, E. (eds) Co-evolution of Intelligent Socio-technical Systems. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36614-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-36614-7_5
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36613-0
Online ISBN: 978-3-642-36614-7
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)