Types of Simulation

Chapter
Part of the Understanding Complex Systems book series (UCS)

Why Read This Chapter?

To understand the different ways that computer simulation can differ in terms of (a) purpose, (b) targets for simulation, (c) what is represented, and (d) its implementation; and subsequently, to be more aware of the choices to be made when simulating social complexity.

Abstract

This chapter describes the main purposes of computer simulation and gives an overview of the main issues that should be regarded when developing computer simulations. While there are two basic ways of representing a system in a simulation model – the equation-based or macroscopic approach and the individual-based or microscopic approach – this chapter (as the rest of the handbook) focuses on the latter. It discusses the various options a modeller faces when choosing how to represent individuals, their interactions and their environment in a simulation model.

References

  1. Amblard F, Quattrociocchi W (2013) Social networks and spatial distribution. Chapter 16 in this volumeGoogle Scholar
  2. Anderson JR et al (2004) An integrated theory of the mind. Psychol Rev 111(4):1036–1060CrossRefGoogle Scholar
  3. Barreteau O et al (2013) Participatory approaches. Chapter 10 in this volumeGoogle Scholar
  4. Bazzan ALC, Bordini RH (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. ACM Press, New York, pp 292–299Google Scholar
  5. Bratman ME (1987) Intentions, plans, and practical reason. Harvard University Press, Cambridge, MAGoogle 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. ACM Press, New York, pp 9–16Google Scholar
  7. Chen D et al (2008) Large-scale agent-based simulation on the grid. Future Gen Comput Syst 24(7):658–671CrossRefGoogle Scholar
  8. Conte R, Gilbert N (eds) (1995) Artificial societies: the computer simulation of social life. UCL Press, LondonGoogle Scholar
  9. David N (2013) Validating simulations. Chapter 8 in this volumeGoogle Scholar
  10. Davidsson P (2000) Multi agent based simulation: beyond social simulation. In: Moss S, Davidsson P (eds) Multi agent based simulation (Lecture notes in computer science, 1979). Springer, Berlin, pp 98–107Google Scholar
  11. Dignum V (2013) Organisational design. Chapter 20 in this volumeGoogle Scholar
  12. Edmonds B, Lucas P, Rouchier J, Taylor R (2013) Human societies: understanding observed social phenomena. Chapter 26 in this volumeGoogle Scholar
  13. Gardner M (1970) Mathematical games: the fantastic combinations of John Conway’s new solitaire game “Life”. Sci Am 223(4):120–124CrossRefGoogle Scholar
  14. Geller A, Moss S (2013) Modeling power and authority: an emergentist view from Afghanistan. Chapter 25 in this volumeGoogle Scholar
  15. Georgeff M, Pell B, Pollack M, Tambe M, Wooldridge M (1998) The belief-desire-intention model of agency. In: Muller J, Singh M, Rao A (eds) Intelligent agents V (Lecture notes in artificial intelligence, 1555). Springer, Berlin, pp 1–10Google Scholar
  16. Gilbert N (2006) When does social simulation need cognitive models? In: Sun R (ed) Cognition and multi-agent interaction: from cognitive modelling to social simulation. Cambridge University Press, Cambridge, pp 428–432Google Scholar
  17. Gilbert N, Doran J (eds) (1994) Simulating societies. UCL Press, LondonGoogle Scholar
  18. Gilbert N, Troitzsch KG (2005) Simulation for the social scientist, 2nd edn. Open University Press/McGraw Hill Education, MaidenheadGoogle Scholar
  19. Gilbert N, Pyka A, Ahrweiler P (2001) Innovation networks: a simulation approach. J Artif Soc Soc Simulat 4(3). http://jasss.soc.surrey.ac.uk/4/3/8.html
  20. Grimm V et al (2006) A standard protocol for describing individual-based and agent-based models. Ecol Model 198:115–126CrossRefGoogle Scholar
  21. Guye-Vuillème A (2004) Simulation of nonverbal social interaction and small groups dynamics in virtual environments (Ph.D. thesis). Ècole Polytechnique Fédérale de Lausanne, No 2933Google Scholar
  22. Hales D (2013) Distributed computer systems. Chapter 21 in this volumeGoogle Scholar
  23. Hemelrijk C (2013) Animal social behaviour. Chapter 22 in this volumeGoogle Scholar
  24. Janssen MA, Jager W (1999) An integrated approach to simulating behavioural processes: a case study of the lock-in of consumption patterns. J Artif Soc Soc Simulat 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):761–768CrossRefGoogle Scholar
  26. Le Page C et al (2013) Environmental management. Chapter 19 in this volumeGoogle Scholar
  27. Massaguer D, Balasubramanian V, Mehrotra S, Venkatasubramanian N (2006) Multi-agent simulation of disaster response. In: Jennings NR, Tambe M, Ishida T, Ramchurn SD (eds) First international workshop on agent technology for disaster management. Hakodate, 8 May 2006, 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, 2792). Springer, Berlin, pp 325–332Google Scholar
  29. Newell A (1994) Unified theories of cognition. Harvard University Press, Cambridge, MAGoogle Scholar
  30. Railsback SF, Grimm V (2011) Agent-based and individual-based modeling: a practical introduction. Princeton University Press, PrincetonGoogle Scholar
  31. Railsback SF, Lytinen SL, Jackson SK (2006) Agent-based simulation platforms: review and development recommendations. Simulation 82(9):609–623CrossRefGoogle Scholar
  32. Ramstedt L, Törnquist Krasemann J, Davidsson P (2013) Movement of people and goods. Chapter 24 in this volumeGoogle Scholar
  33. Reynolds CW (1987) Flocks, herds, and schools: a distributed behavioural model. Comput Graph 21(4):25–34CrossRefGoogle Scholar
  34. Rouchier J (2013) Markets. Chapter 23 in this volumeGoogle Scholar
  35. Sawyer RK (2003) Artificial societies: multi-agent systems and the micro–macro link in sociological theory. Sociol Methods Res 31(3):325–363MathSciNetCrossRefGoogle Scholar
  36. Schelling TC (1971) Dynamic models of segregation. J Math Sociol 1:143–186CrossRefGoogle Scholar
  37. Schiff JL (2008) Cellular automata: a discrete view of the world. Wiley, OxfordMATHGoogle Scholar
  38. Schüle M, Herrler R, Klügl F (2004) Coupling GIS and multi-agent simulation: towards infrastructure for realistic simulation. In: Lindemann G, Denzinger J, Timm IJ, Unland R (eds) Multiagent system technologies, second German conference, MATES 2004 (LNCS, 3187). Springer, Berlin, pp 228–242Google Scholar
  39. Verhagen H (2001) Simulation of the learning of norms. Soc Sci Comput Rev 19(3):296–306CrossRefGoogle Scholar
  40. Williams R (1993) An agent based simulation environment for public order management training. In: Western simulation multiconference, object-oriented simulation conference, Hyatt Regency, La Jolla, 17–20 Jan 1993, pp 151–156Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceMalmö UniversityMalmöSweden
  2. 2.Stockholm UniversityStockholmSweden

Personalised recommendations