Weitere Kapitel dieses Buchs durch Wischen aufrufen
We present an Agent-Based model called ProtestLab for the simulation of street protests, with multiple types of agents (protesters, police and ‘media’) and scenario features (attraction points, obstacles and entrances/exits). In ProtestLab agents can have multiple “personalities” (implemented via agent subtypes), goals and possible states, including violent confrontation. The model includes quantitative measures of emergent crowd patterns, protest intensity, police effectiveness and potential ‘news impact’, which can be used to compare simulation outputs with estimates from videos of real protests for parametrization and validation. ProtestLab was applied to a scenario of policemen defending a government building from protesters (typical of anti-austerity protests in front of the Parliament in Lisbon, Portugal) and reproduced many features observed in real events, such as clustering of ‘active’ and ‘violent’ protesters, formation of moving confrontation lines, occasional fights and arrests, ‘media’ agents wiggling around ‘hot spots’ and policemen with defensive or offensive behaviour.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Collins, R. (2008). Violence: A micro-sociological theory. Princeton: Princeton University Press. CrossRef
Collins, R. (2009). Micro and macro causes of violence. International Journal of Conflict and Violence, 3(1), 9–22.
Comninos, A. (2011). Twitter revolutions and cyber crackdowns. User-generated content and social networking in the Arab spring and beyond. Technical report, Association of Progressive Communication (PAC).
Davies, T. P., Fry, H. M., Wilson, A. G., & Bishop, S. R. (2013). A mathematical model of the london riots and their policing. Scientific Reports, 3, 1303.
Durupınar, F. (2010). From audiences to mobs: Crowd simulation with psychological factors. PhD thesis, Bılkent University.
Epstein, J. M. (2002). Modeling civil violence: An agent-based computational approach. Proceedings of the National Academy of Sciences of the United States of America, 99, 7243–7250.
Epstein, J. M., Steinbruner, J. D., & Parker, M. T. (2001). Modeling civil violence: An agent-based computational approach. Center on Social and Economic Dynamics, Working Paper No. 20.
Fonoberova, M., Fonoberov, V. A., Mezic, I., Mezic, J., & Brantingham, P. J. (2012). Nonlinear dynamics of crime and violence in urban settings. Journal of Artificial Societies and Social Simulation, 15(1).
Gilbert, N. (2007). Agent-based models (quantitative applications in the social sciences). London: SAGE Publications.
Gilbert, N., & Troitzsch, K. G. (2005). Simulation for the social scientist (2nd ed.). Buckingham: Open University Press.
Goh, C. K., Quek, H. Y., Tan, K. C., & Abbass, H. A. (2006). Modeling civil violence: An evolutionary multi-agent, game theoretic approach. In IEEE Congress on Evolutionary Computation (pp. 1624–1631). Piscataway: IEEE.
Gurr, T. R. (1968). Psychological factors in civil violence. World Politics, 20(2), 245–278. CrossRef
Helbing, D., & Molnár, P. (1995). Social force model for pedestrian dynamics. Physical Review E, 51(5), 4282–4286. CrossRef
Helbing, D., Molnár, P., Farkas, I., & Bolay, K. (2001). Self-organizing pedestrian movement. Environment and Planning B: Planning and Design, 28, 361–383. CrossRef
Ilachinsky, A. (2004). Artificial war. Multiagent-based simulation of combat. Singapore: World Scientific. CrossRef
Jager, W., Popping, R., & van de Sande, H. (2001). Clustering and fighting in two-party crowds: Simulating the approach-avoidance conflict. Journal of Artificial Societies and Social Simulation, 4(3).
Kim, J. W., & Hanneman, R. A. (2011). A computational model of worker protest. Journal of Artificial Societies and Social Simulation, 14(3).
Klandermans, B., van Stekelenburg, J., & Walgrave, S. (2014). Comparing street demonstrations. International Sociology, 29(6), 493–503. CrossRef
Klüpfel, H. L. (2003). A cellular automaton model for crowd movement and egress simulation. PhD thesis, Universität Duisburg-Essen.
Kuran, T. (1989). Sparks and prairie fires: A theory of unanticipated political revolution. Public Choice, 61, 41–74. CrossRef
Lacko, P., Ort, M., Kyžňanský, M., Kollár, A., Pakan, F., Ošvát, M., et al. (2013). Riot simulation in urban areas. In 14th IEEE International Symposium on Computational Intelligence and Informatics (pp. 488–492).
Leggett, R. (2004). Real-time crowd simulation: A review. available online at http://www.leggettnet.org.uk/docs/crowdsimulation.pdf, accessed on May 17, 2016.
Lemos, C., Coelho, H., & Lopes, R. J. (2013). Agent-based modeling of social conflict, civil violence and revolution: State-of-the-art review and further prospects. In Proceedings of the Eleventh European Workshop on Multi-Agent Systems (EUMAS 2013), Toulouse, France (pp. 124–138).
Lemos, C., Coelho, H., & Lopes, R. J. (2014). Agent-based modeling of protests and violent confrontation: A micro-situational, multi-player, contextual rule-based approach. In Proceedings of the 5th World Congress on Social Simulation, São Paulo, Brazil (pp. 136–160).
Lemos, C., Lopes, R. J., & Coelho, H. (2015). Quantitative measures of crowd patterns in agent-based models of street protests. In 2015 Third World Conference on Complex Systems, Marrakech, Morocco (pp. 1–6).
Makowsky, M. D., & Rubin, J. (2011). An agent-based model of centralized institutions, social network technology, and revolution. Working Paper 2011-05, Towson University.
Parunak, H. V. D., Brooks, S. H., Brueckner. S., & Gupta, R. (2014). Dynamically tracking the real world in an agent-based model. In S. J. Alam & H. V. D. Parunak (Eds.), Fourteenth International Workshop on Multi-Agent-Based Simulation (MABS 2013) at AAMAS 2013. Lecture notes in artificial intelligence (vol. 8235, pp. 3–16). Berlin: Springer.
Pelechano, N., Allbeck, J. M., & Badler, N. I. (2007). Controlling individual agents in high-density crowd simulation. In Eurographics/ACM SIGGRAPH Symposium on Computer Animation (pp. 99–108).
Schmidt, B. (2002). Modelling of human behaviour. the pecs reference model. In A. Verbraeck & W. Krug (Eds.), Proceedings 14th European Simulation Symposium.
Still, G. K. (2000). Crowd dynamics. PhD thesis, University of Warwick.
Torrens, P. M., & McDaniel, A. W. (2013). Modeling geographic behavior in riotous crowds. Annals of the Association of American Geographers, 103(1), 20–46. CrossRef
TSO. (2010a). Understanding crowd behaviours, Volume 1 - practical guidance and lessons identified (Cabinet Office ed.). London: The Stationery Office.
TSO. (2010b). Understanding crowd behaviours, Volume 2 - supporting theory and evidence (Cabinet Office ed.). London: The Stationery Office.
Wikström, P. O. H., & Treiber, K. H. (2009). Violence as situational action. International Journal of Conflict and Violence, 3(1), 75–96.
Wooldridge, M. (2009). An introduction to multiagent systems (2nd ed.). New York: Wiley.
- ProtestLab: A Computational Laboratory for Studying Street Protests
Carlos M. Lemos
Rui J. Lopes
in-adhesives, MKVS, Neuer Inhalt/© Zühlke, Neuer Inhalt/© momius | stock.adobe.com