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Types of Simulation

  • Paul Davidsson
  • Harko Verhagen
Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

This looks at various ways that computer simulations can differ not in terms of their detailed mechanisms but in terms of its broader purpose, structure, ontology (what is represented), and approach to implementation. It starts with some different roles of people that may be concerned with a simulation and goes on to look at some of the different contexts within which a simulation is set (thus implying its use or purpose). It then looks at the kinds of system that might be simulated. Shifting to the modelling process, it looks at the role of the individuals within the simulations, the interactions between individuals, and the environment that they are embedded within. It then discusses the factors to consider in choosing a kind of model and some of the approaches to implementing it.

Keywords

Environment model Implementation Individual model Interaction model Purposes Users 

References

  1. Amblard, F., & Quattrociocchi, W. (2017). Social networks and spatial distribution. doi:https://doi.org/10.1007/978-3-319-66948-9_19.Google Scholar
  2. Anderson, J. R., et al. (2004). An integrated theory of the mind. Psychological Review, 111(4), 1036–1060.CrossRefGoogle Scholar
  3. Barreteau, O., Bots, P., Daniell, K., Etienne, M., Perez, P., Barnaud, C., et al. (2017). Participatory approaches. In B. Edmonds & R. Meyer (Eds.), Simulating social complexity: A handbook. Berlin: Springer-Verlag.Google Scholar
  4. Bazzan, A.L.C., & Bordini, R.H. (2001). A framework for the simulation of agents with emotions: Report on experiments with the iterated prisoners dilemma. In Fifth international conference on autonomous agents, Montreal, 2001 (pp. 292–299). New York: ACM Press.Google Scholar
  5. Bratman, M. E. (1987). Intentions, plans, and practical reason. Cambridge, MA: Harvard University Press.Google Scholar
  6. Broersen, J., Dastani, M., Huang, Z., Hulstijn, J., & Van der Torre, L. (2001). The BOID architecture: Conflicts between beliefs, obligations, intentions and desires. In Fifth international conference on autonomous agents, Montreal, 2001 (pp. 9–16). New York: ACM Press.Google Scholar
  7. Chen, D., Theodoropoulos, G. K., Turner, S. J., Cai, W., Minson, R., & Zhang, Y. (2008). Large-scale agent-based simulation on the grid. Future Generation Computer Systems, 24(7), 658–671.CrossRefGoogle Scholar
  8. Conte, R., & Gilbert, N. (Eds.). (1995). Artificial societies: The computer simulation of social life. London: UCL Press.Google Scholar
  9. David, N., Fachada, N., & Rosa, A. C. (2017). Verifying and validating simulations. In B. Edmonds & R. Meyer (Eds.), Simulating social complexity: A handbook. Berlin: Springer-Verlag.Google Scholar
  10. Davidsson, P. (2000). Multi agent based simulation: Beyond social simulation. In S. Moss & P. Davidsson (Eds.), Multi agent based simulation, Lecture notes in computer science (Vol. 1979, pp. 98–107). Berlin: Springer.Google Scholar
  11. Dignum, V. (2013). Organisational design. In B. Edmonds & R. Meyer (Eds.), Simulating social complexity – A handbook (pp. 541–562). Berlin: Springer.CrossRefGoogle Scholar
  12. Edmonds, B., Lucas, P., Rouchier, J., & Taylor, R. (2017). Human societies: Understanding observed social phenomena. In B. Edmonds & R. Meyer (Eds.), Simulating social complexity: A handbook. Berlin: Springer-Verlag.CrossRefGoogle Scholar
  13. Gardner, M. (1970). Mathematical games: The fantastic combinations of John Conway’s new solitaire game “Life”. Scientific American, 223(4), 120–124.CrossRefGoogle Scholar
  14. Geller, A., & Moss, S. (2017). Modeling power and authority: An emergentist view from Afghanistan. In B. Edmonds & R. Meyer (Eds.), Simulating social complexity: A handbook. Berlin: Springer-Verlag.Google Scholar
  15. Georgeff, M., Pell, B., Pollack, M., Tambe, M., & Wooldridge, M. (1998). The belief-desire-intention model of agency. In J. Muller, M. Singh, & A. Rao (Eds.), Intelligent agents V, Lecture notes in artificial intelligence (Vol. 1555, pp. 1–10). Berlin: Springer.Google Scholar
  16. Gilbert, N. (2006). When does social simulation need cognitive models? In R. Sun (Ed.), Cognition and multi-agent interaction: From cognitive modelling to social simulation (pp. 428–432). Cambridge: Cambridge University Press.Google Scholar
  17. Gilbert, N., & Doran, J. (Eds.). (1994). Simulating societies. London: UCL Press.Google Scholar
  18. Gilbert, N., Pyka, A., & Ahrweiler, P. (2001). Innovation networks: A simulation approach. Journal of Artificial Societies and Social Simulation, 4(3). http://jasss.soc.surrey.ac.uk/4/3/8.html
  19. Gilbert, N., & Troitzsch, K. G. (2005). Simulation for the social scientist (2nd ed.). Maidenhead: Open University Press & McGraw Hill Education.Google Scholar
  20. Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., et al. (2006). A standard protocol for describing individual-based and agent-based models. Ecological Modelling, 198, 115–126.CrossRefGoogle Scholar
  21. Guye-Vuillème, A. (2004). Simulation of nonverbal social interaction and small groups dynamics in virtual environments. PhD thesis, Ècole Polytechnique Fédérale de Lausanne, No 2933.Google Scholar
  22. Hales, D. (2017). Distributed computer systems. doi:https://doi.org/10.1007/978-3-319-66948-9_23.Google Scholar
  23. Hemelrijk, C. (2017). Animal social behaviour. doi:https://doi.org/10.1007/978-3-319-66948-9_24.Google Scholar
  24. Janssen M.A., & Jager, W. (1999). An integrated approach to simulating behavioural processes: A case study of the lock-in of consumption patterns. Journal of Artificial Societies and Social Simulation, 2(2). http://jasss.soc.surrey.ac.uk/2/2/2.html
  25. Künzel, J., & Hämmer, V. (2006). Simulation in university education: The artificial agent PSI as a teaching tool. Simulation, 82(11), 761–768.CrossRefGoogle Scholar
  26. Le Page, C., Bazile, D., Becu, N., Bommel, P., Bousquet, F., Etienne, M., et al. (2017). Agent-based modelling and simulation applied to environmental management. doi:https://doi.org/10.1007/978-3-319-66948-9_22.Google Scholar
  27. Massaguer, D., Balasubramanian, V., Mehrotra, S., & Venkatasubramanian, N. (2006, May 8). Multi-agent simulation of disaster response. In N.R. Jennings, M. Tambe, T. Ishida, & S.D. Ramchurn (Eds.), First international workshop on agent technology for disaster management, Hakodate, Hokkaido, Japan (pp. 124–130). http://users.ecs.soton.ac.uk/sdr/atdm/ws34atdm.pdf
  28. Méndez, G., Rickel, J., & de Antonio, A. (2003). Steve meets Jack: The integration of an intelligent tutor and a virtual environment with planning capabilities. In Intelligent virtual agents, Lecture notes on artificial intelligence (Vol. 2792, pp. 325–332). Berlin: Springer.CrossRefGoogle Scholar
  29. Newell, A. (1994). Unified theories of cognition. Cambridge, MA: Harvard University Press.Google Scholar
  30. Railsback, S. F., Lytinen, S. L., & Jackson, S. K. (2006). Agent-based simulation platforms: Review and development recommendations. Simulation, 82(9), 609–623.CrossRefGoogle Scholar
  31. Railsback, S. F., & Grimm, V. (2011). Agent-based and individual-based modeling: A practical introduction. Princeton: Princeton University Press.MATHGoogle Scholar
  32. Ramstedt, L., Törnquist Krasemann, J., & Davidsson, P. (2017). Movement of people and goods. doi:https://doi.org/10.1007/978-3-319-66948-9_26.Google Scholar
  33. Reynolds, C. W. (1987). Flocks, herds, and schools: A distributed behavioural model. Computer Graphics, 21(4), 25–34.CrossRefGoogle Scholar
  34. Rouchier, J. (2017). Agent-Based simulation as a useful tool for the study of markets. doi:https://doi.org/10.1007/978-3-319-66948-9_25.Google Scholar
  35. Sawyer, R. K. (2003). Artificial societies: Multi-agent systems and the micro-macro link in sociological theory. Sociological Methods & Research, 31(3), 325–363.MathSciNetCrossRefGoogle Scholar
  36. Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1, 143–186.CrossRefMATHGoogle Scholar
  37. Schiff, J. L. (2008). Cellular automata: A discrete view of the world. Oxford: Wiley.MATHGoogle Scholar
  38. Schüle, M., Herrler, R., & Klügl, F. (2004). Coupling GIS and multi-agent simulation: Towards infrastructure for realistic simulation. In G. Lindemann, J. Denzinger, I.J. Timm, & R. Unland (Eds.), Multiagent system technologies, second German conference, MATES 2004, LNCS (Vol. 3187, pp. 228–242). Berlin: Springer.Google Scholar
  39. Verhagen, H. (2001). Simulation of the learning of norms. Social Science Computer Review, 19(3), 296–306.CrossRefGoogle Scholar
  40. Williams, R. (1993). An agent based simulation environment for public order management training. In Western simulation multiconference, object-oriented simulation conference (pp. 151–156).Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Malmö UniversityMalmöSweden
  2. 2.Stockholm UniversityStockholmSweden

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