Abstract
Simulative models of past behavior generally comprise many parameters or factors affecting model outcome. The interconnectedness and value settings of model parameters and factors form a source of potential uncertainty. The main question that has to be addressed in relation to the problem of uncertainty is: What extent do model connections and value settings have on the model's outcome, and how can outcomes be related to “real-world” observations? Few archaeological examples exist that seriously deal with this question. Additionally, the input data utilized in predictive and simulative models can amplify error as a result of archaeological survey bias and a lack of accuracy or applicability of environmental data, such as digital elevation models, soil maps, and groundwater tables. An “easy” response to the problem of uncertainty is to increase resolution, stemming from the uncritical assumption that more data and model parameters will produce more detailed and hence more reliable outcomes. In this paper, a preliminary methodology for developing sensitivity analyses for an archaeological model of hunter-gatherer behavior in the central Netherlands is presented that attempts to evaluate where, when, and how much error is introduced to the model at different stages of the multi-ordered, cascading framework. Consideration is also given to how this particular model, and archaeological models in general, may be refined or improved after the uncertainty analysis is complete.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
All dates from here are reported as uncalibrated radiocarbon dates.
- 2.
This number was chosen because it represents the least number of combinations that are still mathematically distinct for parameters scaled between four suitability values, 0–3.
References
Barton, C. M., Ullah, I. I. T., Bergin, S. M., Mitasova, H., & Sarjoughian, H. (2012). Looking for the future in the past: Long-term change in socioecological systems. Ecological Modelling, 214, 42–53.
Binford, L. R. (1980). Willow smoke and dog’s tails: Hunter-gatherer settlement systems and archaeological site formation. American Antiquity, 45, 4–20.
Brantingham, P. J. (2003). A neutral model of stone raw material procurement. American Antiquity, 68(3), 587–509.
Brouwer, M. E. (2011). Modeling Mesolithic hunter-gatherer land use and post-glacial landscape dynamics in the Central Netherlands. Ph.D. Thesis, Department of Anthropology, Michigan State University.
Brouwer Burg, M. E. (2013). Reconstructing “total” paleo-landscapes for archaeological investigation: An example from the central Netherlands. Journal of Archaeological Science, 40, 2308–2320.
Buffon, G.-L. Leclerc, Comte de (1749–1804). Histoire naturelle, générale et particulière. Paris: l’ Imprimerie Royale.
Camerer, C., & Weber, M. (1992). Recent development in modeling preferences: Uncertainty and ambiguity. Journal of Risk and Uncertainty, 5, 325–370.
Danielisová, A., & Pokorný, P. (2011). Pollen and archaeology in GIS. Theoretical considerations and modified approach testing. In P. Verhagen, A. G. Posluschny, & A. Danielisová (Eds.), Go your own least cost path: Spatial technology and archaeological interpretation (Proceedings of the GIS session at EAA 2009, Riva del Garda, September 2009. BAR International Series 2284. Riva del Garda, Italy). Oxford, UK: Archaeopress.
Doran, J. (2008). Review of “the model-based archaeology of socionatural systems”. Journal of Artificial Societies and Social Simulation, 11, 1–4.
Eastman, J. R. (1999). IDRISI 32: Guide to GIS and image processing (Software manual, Vol. 2). Worcester, England: Clark Labs, Clark University.
Gilbert, N. (2008). Agent based models. Thousand Oaks, CA: Sage Research Methods.
Happe, K., Kellerman, K., & Balmann, A. (2006). Agent-based analysis of agricultural policies: An illustration of the agricultural policy simulator AgriPoliS, its adaptation, and behavior. Ecology and Society, 11(1), 49.
Jankowski, P., Andrienko, N., & Andrienko, G. (2001). Map-centered exploratory approach to multiple criteria spatial decision making. International Journal of Geographical Information Science, 15(2), 101–127.
Kelly, R. L. (1995). The foraging spectrum: Diversity in hunter-gatherer lifeways. Washington, DC: Smithsonian Institution Press.
Krist, F. J. J. (2001). A predictive model of Paleo-Indian subsistence and settlement. Ph.D. Thesis, Department of Anthropology, Michigan State University.
Lake, M. W. (2000). MAGICAL computer simulation of Mesolithic foraging. In T. A. Kohler & G. J. Gumerman (Eds.), Dynamics in human and primate societies: Agent-based modelling of social and spatial processes (pp. 107–143). New York: Oxford University Press.
Laughlin, W. S. (1980). Aleuts: Survivors of the Bering Land Bridge. New York: Holt, Rinehart and Winston.
Leclerc, G.-L. (1749–1804). Histoire naturelle, générale et partiulière. Paris: Imprimerie Royale.
Lenhart, T., Eckhart, K., Fohrer, N., & Frede, H.-G. (2002). Comparison of two different approaches of sensitivity analysis. Physics and Chemistry of the Earth, 27, 645–654.
Peeters, J. H. M. (2007). Hoge Vaart-A27 in context: Towards a model of Mesolithic–Neolithic land use dynamics as a framework for archaeological heritage management. Ph.D. Thesis, Department of Archaeology, University of Amsterdam, Amsterdam.
Premo, L. S. (2006). Patchiness and prosociality modeling: The evolution and archaeology of plio-Pleistocene Hominin food sharing. Ph.D. Thesis, University of Arizona, Tuscon, Arizona.
Rogers, E. (1967). Subsistence areas of the Cree-Ojibwa of the Eastern Subarctic: A preliminary study. National Museum of Canada Bulletin, 204, 59–90.
Rogers, E. (1969). Natural environment—Social organization—Witchcraft: Cree versus Ojibway—A test case. In D. Damas (Ed.), Contributions to anthropology: Ecological essays (National Museum of Canada Bulletin 230, pp. 24–39). Ottawa, ON: National Museum of Canada.
Rogers, J. D., Nichols, T., Emmerich, T., Latek, M., & Cioffi-Revilla, C. (2012). Modeling scale and variability in human–environmental interactions in Inner Asia. Ecological Modelling, 241, 5–14.
Saisana, M., Saltelli, S., & Tarantola, S. (2005). Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators. Journal of Royal Statistical Society, Series A, 168(2), 307–323.
Savage, L. J. (1954). The foundations of statistics. New York: Wiley.
van den Biggelaar, D., Kluiving, S., Kasse, K., & Kolen, J. (2014). Why would we need archaeological remains? Modelling Late Glacial land use without archaeological traces, a case study from Flevoland (central Netherlands). Poster presented at the 3rd International Landscape Conference, Rome, Italy. 17th–20th September, 2014.
van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Diluzio, M., & Srinivasan, R. (2006). A global sensitivity analysis tool for the parameters of multi-variable catchment models. Journal of Hydrology, 324, 10–23.
von Neumann, J., & Morgenstern, O. (1947). Theory of games and economic behavior (2nd ed.). Princeton, NJ: Princeton University Press.
Vonk Noordegraaf, A., Nielen, M., & Kleijnen, J. P. C. (2003). Sensitivity analysis by experimental design. European Journal of Operational Research, 146, 433–443.
Whitley, T. G. (2000). Dynamical systems modeling in archaeology: A GIS approach to site selection processes in the greater Yellowstone region. Ph.D. Thesis, Department of Anthropology, University of Pittsburg, Pittsburg.
Whitley, T. G. (2005). A brief outline of causality-based cognitive archaeological probabilistic modeling. In M. van Leusen & H. Kamermans (Eds.), predictive modeling for archaeological heritage management: A research agenda, Vol. 29 (pp. 123-138). Rijksdienst voor het Oudheidkundig Bodemonderzoek (ROB), Amersfoort.
Whitley, T. G., Moore, G., Goel, G., & Jackson, D. (2009). Beyond the marsh: Settlement choice, perception and spatial decision-making on the Georgia coastal plain. In B. Frischer, J. Crawford, & D. Kollers (Eds.), Making history interactive: Proceedings of the 37th computer applications and quantitative methods in archaeology (CAA) Conference, Williamsburg, VA (pp. 380–390). Oxford, UK: Archaeopress.
Wobst, H. M. (1974). Boundary conditions for Paleolithic social systems: A simulation approach. American Antiquity, 39(2), 147–178.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Brouwer Burg, M. (2016). GIS-Based Modeling of Archaeological Dynamics (GMAD): Weaknesses, Strengths, and the Utility of Sensitivity Analysis. In: Brouwer Burg, M., Peeters, H., Lovis, W. (eds) Uncertainty and Sensitivity Analysis in Archaeological Computational Modeling. Interdisciplinary Contributions to Archaeology. Springer, Cham. https://doi.org/10.1007/978-3-319-27833-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-27833-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27831-5
Online ISBN: 978-3-319-27833-9
eBook Packages: Social SciencesSocial Sciences (R0)