Simulations

  • Oliver Nakoinz
  • Daniel Knitter
Chapter
First Online:
Part of the Quantitative Archaeology and Archaeological Modelling book series (QAAM)

Abstract

Simulations are usually seen as an advanced type of models. They are empirical models of artificial data generated according to theoretical rules, attempting to imitate real processes or structures by applying certain rules on random data or observations. The final model uses techniques to reconstruct empirical models with the input of the artificial data. The rather complex relationship between the empirical and theoretical components sometimes makes it difficult to interpret the results. Stochastic simulations like Monte Carlo simulations use random numbers of a certain distribution rather than real observations. Simulations of point processes bear great potential for archaeological problems, since they are direct comparable with archaeological sources. Grid-based simulations have many restrictions but are useful for studying certain phenomena. Agent-based modelling applies a set of behaviour rules to agents—entities capable of behaviour—in an iterative process, while multi-agent models allow the interaction of agents.

Keywords

Simulation Random number Point process simulations Point based simulations Grid based simulations Cellular automata Agent based simulations 
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References

  1. 1.
    Baddeley, A., & Turner, R. (2005). Spatstat: An R package for analyzing spatial point patterns. Journal of Statistical Software, 12, 1–42.CrossRefGoogle Scholar
  2. 2.
    Barceló, J., Cuesta J., Del Castillo, F., Galán, J., Mameli, L., Miguel, F., et al. (2013). Simulating prehistoric ethnicity: The case of Patagonian Hunter-Gatherers. In F. Contreras & F. J. Melero (Eds.), CAA 2010: Fusion of cultures, bar international series (Vol. S2494, pp. 137–144). Oxford: ArcheoPress.Google Scholar
  3. 3.
    Danielisová, A., Olševičová, K., Cimler, R., & Machálek, T. (2015). Understanding the iron age economy: sustainability of agricultural practices under stable population growth. In G. Wurzer, K. Kowarik & H. Reschreiter (Eds.), Agent-based Modeling and simulation in archaeology (pp. 183–216). New York: Springer.Google Scholar
  4. 4.
    Daudé, E. (2004) Apports de la simulation multi-agents à l’étude des processus de diffusion. Cybergeo: Revue Europenne de Gographie, 255, 15. http://cybergeo.revues.org/3835; doi: 10.4000/cybergeo.3835.
  5. 5.
    Epstein, J. M. (1999). Agent-based computational models and generative social science. Complexity, 4(5), 41–60.CrossRefGoogle Scholar
  6. 6.
    Fonstad, M. A. (2006). Cellular automata as analysis and synthesis engines at the geomorphologyecology interface. Geomorphology, 77, 217–234 (2006).CrossRefGoogle Scholar
  7. 7.
    Gardner, M. (1970). Mathematical Games the fantastic combinations of John Conway’s new solitaire game “life”. Scientific American, 223, 120–123.CrossRefGoogle Scholar
  8. 8.
    Hägerstrand, T. (1967). Innovation diffusion as a spatial process. Chicago: University of Chicago Press.Google Scholar
  9. 9.
    Hepppenstall, A. J., Crooks, A. T., See, L. M., & Batty, M. (Eds.). (2012). Agent-based models of geographical systems. New York: Springer.Google Scholar
  10. 10.
    Kohler, T. A., & Gumerman, G. J. (2000). Dynamics in human and primate societies: Agent-based modeling of social and spatial processes. Oxford: Oxbow.Google Scholar
  11. 11.
    Lake, M. W. (2014). Trends in archaeological simulation. Journal of Archaeological Method and Theory, 21, 258–287.CrossRefGoogle Scholar
  12. 12.
    Mayer, J., Schmidt, V., & Schweiggert, F. (2004). A unified framework for spatial stochastic models. Simulation Modelling Practice and Theory, 12, 307–326.CrossRefGoogle Scholar
  13. 13.
    Møller, J., & Waagepetersen, R. P. (2004). Statistical inference and simulation for spatial point processes. Boca Raton: Chapman & Hall/CRC.Google Scholar
  14. 14.
    O’Sullivan, D., & Perry, G. L. W. (2013). Spatial simulation: Exploring pattern and process. Chichester: Wiley.CrossRefGoogle Scholar
  15. 15.
    Packard, N. H., & Wolfram, S. (1985). Two-dimensional cellular automata. Journal of Statistical Physics, 38, 901–946.CrossRefGoogle Scholar
  16. 16.
    Petzoldt, T., & Rinke, K. (2007). Simecol: An object-oriented framework for ecological modeling in R. Journal of Statistical Software, 22(9), 1–31.CrossRefGoogle Scholar
  17. 17.
    Premo, L. S. (2008). Exploring behavioral terra incognita with archaeological agent-based models. In B. Frischer & A. Dakouri-Hild (Eds.), Beyond illustration: 2D and 3D technologies as tools of discovery in archaeology. British Archaeological Reports International Series (pp. 46–138). Oxford: ArchaeoPress.Google Scholar
  18. 18.
    Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(2), 234–240.CrossRefGoogle Scholar
  19. 19.
    Velten, K. (2009). Mathematical modeling and simulation. Weinheim: VLC.Google Scholar
  20. 20.
    Wurzer, G., Kowarik, K., & Reschreiter, H. (Eds.). (2015). Agent-based modeling and simulation in archaeology. New York: Springer.Google Scholar
  21. 21.
    Zubrow, E. (1981). Simulation as a heuristic device in archaeology. In J. A. Sabloff (Ed.), Simulations in archaeology (pp. 143–188). Albuquerque: University of New Mexico Press.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Oliver Nakoinz
    • 1
  • Daniel Knitter
    • 2
  1. 1.University of KielKielGermany
  2. 2.Excellence Cluster TopoiFreie UniversitätBerlinGermany

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