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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baddeley, A., & Turner, R. (2005). Spatstat: An R package for analyzing spatial point patterns. Journal of Statistical Software, 12, 1–42.
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.
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.
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.
Epstein, J. M. (1999). Agent-based computational models and generative social science. Complexity, 4(5), 41–60.
Fonstad, M. A. (2006). Cellular automata as analysis and synthesis engines at the geomorphologyecology interface. Geomorphology, 77, 217–234 (2006).
Gardner, M. (1970). Mathematical Games the fantastic combinations of John Conway’s new solitaire game “life”. Scientific American, 223, 120–123.
Hägerstrand, T. (1967). Innovation diffusion as a spatial process. Chicago: University of Chicago Press.
Hepppenstall, A. J., Crooks, A. T., See, L. M., & Batty, M. (Eds.). (2012). Agent-based models of geographical systems. New York: Springer.
Kohler, T. A., & Gumerman, G. J. (2000). Dynamics in human and primate societies: Agent-based modeling of social and spatial processes. Oxford: Oxbow.
Lake, M. W. (2014). Trends in archaeological simulation. Journal of Archaeological Method and Theory, 21, 258–287.
Mayer, J., Schmidt, V., & Schweiggert, F. (2004). A unified framework for spatial stochastic models. Simulation Modelling Practice and Theory, 12, 307–326.
Møller, J., & Waagepetersen, R. P. (2004). Statistical inference and simulation for spatial point processes. Boca Raton: Chapman & Hall/CRC.
O’Sullivan, D., & Perry, G. L. W. (2013). Spatial simulation: Exploring pattern and process. Chichester: Wiley.
Packard, N. H., & Wolfram, S. (1985). Two-dimensional cellular automata. Journal of Statistical Physics, 38, 901–946.
Petzoldt, T., & Rinke, K. (2007). Simecol: An object-oriented framework for ecological modeling in R. Journal of Statistical Software, 22(9), 1–31.
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.
Tobler, W. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(2), 234–240.
Velten, K. (2009). Mathematical modeling and simulation. Weinheim: VLC.
Wurzer, G., Kowarik, K., & Reschreiter, H. (Eds.). (2015). Agent-based modeling and simulation in archaeology. New York: Springer.
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.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Nakoinz, O., Knitter, D. (2016). Simulations. In: Modelling Human Behaviour in Landscapes. Quantitative Archaeology and Archaeological Modelling . Springer, Cham. https://doi.org/10.1007/978-3-319-29538-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-29538-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29536-7
Online ISBN: 978-3-319-29538-1
eBook Packages: Behavioral Science and PsychologyBehavioral Science and Psychology (R0)