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Introduction to Uncertainty and Sensitivity Analysis in Archaeological Computational Modeling

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Uncertainty and Sensitivity Analysis in Archaeological Computational Modeling

Part of the book series: Interdisciplinary Contributions to Archaeology ((IDCA))

Abstract

Computational modeling in archaeology is approaching its 50th anniversary. Despite its longevity, it can be argued that this approach has not matured accordingly, in terms of its contributions to theory building and research design, or general archaeological inquiry. Further, this approach has not undergone the same type of rigorous, self-reflective testing as similar computational approaches have in related fields. Symptomatic of this situation is the short shrift given to model validation and verification in archaeology, particularly in terms of the origins, nature, and magnitude of embedded uncertainties. However, other disciplines routinely apply various forms of sensitivity analysis (SA) as a systematic component of research design that evaluates such uncertainties. Here, the role of and approaches to computational modeling in archaeology is explored. Use of such modeling validation techniques in related fields that target geologic, ecosystems, and social modeling are presented in order to identify translatable insight regarding the detection, acknowledgement, incorporation, and mitigation of uncertainty. The interface between model equifinality and uncertainty are then addressed, with the overall goal of determining the utility of SAs for archaeological computational modeling, as a way to identify model strengths and weaknesses. This synopsis acts as a benchmark for various exemplars of SA in archaeological computational modeling.

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Notes

  1. 1.

    We owe this example to Dr. Henk Weerts of the Cultural Heritage Agency of the Netherlands.

  2. 2.

    An example is the dynamic landscape evolution model LAPSUS developed at Wageningen University.

References

  • Aschwanden, C. (2015). Science isn’t broken. Five ThirtyEight newsletter. ESPN. Retrieved from www.fivethirtyeight.com/features/science-isnt-broken/.

  • Axelrod, R. (1997). The dissemination of culture: A model with local convergence and global polarization. Journal of Conflict Resolution, 41(2), 203–226.

    Article  Google Scholar 

  • Axtell, R., Epstein, J. M., Dean, J. S., Gumerman, G. J., Swedlund, A. C., Harburger, J., et al. (2002). Population growth and collapse in a multi-agent model of the Kayenta Anasazi in Long House Valley. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 99(3), 7275–7279.

    Article  Google Scholar 

  • Batty, M. (2008). The size, scale, and shape of cities. Science, 319(5864), 769–771.

    Article  Google Scholar 

  • Batty, M. (2012). Building a science of cities. Cities, 29, S9–S16.

    Article  Google Scholar 

  • Batty, M., & Longley, P. A. (1994). Fractal cities: A geometry of form and function. San Diego, CA: Academic.

    Google Scholar 

  • Bettinger, R. L. (2008). Cultural transmission and archaeology. In M. J. O’Brien (Ed.), Cultural transmission and archaeology: Issues and case studies (pp. 1–9). Washington, DC: SAA Press.

    Google Scholar 

  • Bettinger, R. L., & Eerkens, J. (1999). Point typologies, cultural transmission, and the spread of bow-and-arrow technology in the prehistoric Great Basin. American Antiquity, 64, 231–242.

    Article  Google Scholar 

  • Berger, T., Goodchild, M., Janssen, M. A., Manson, S. M., Najlis, R., & Parker, D. C. (2001). Methodological considerations for agent-based modeling of land-use and land-cover change. In D. C. Parker, T. Berger, & S. M. Manson (Eds.), Agent-based models of land-use and land-cover change. Report and review of an international workshop. Irvine.

    Google Scholar 

  • Bonner, J. T. (2006). Why size matters: From bacteria to blue whales. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • 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.

    Google Scholar 

  • Brown, C. T., Liebovitch, L. S., & Glendon, R. (2007). Lévy flights in Dobe Ju/’hoansi foraging patterns. Human Ecology, 35, 129–138.

    Article  Google Scholar 

  • Brown, D. G., Riolo, R., Robinson, D. T., North, M. J., & Rand, W. (2005). Spatial process and data models: Toward integration of agent-based models and GIS. Journal of Geographic Systems, 7(1), 25–47.

    Article  Google Scholar 

  • Clark, J. E. (2000). Toward a better explanation of heredity inequality: A critical assessment of natural and historic human agents. In M. A. Dobres & J. E. Robb (Eds.), Agency in archaeology (pp. 92–112). London: Routledge.

    Google Scholar 

  • Cordell, L. S., Kelley, J. H., Kintigh, K. W., Lekson, S. H., & Sinclair, R. M. (1994). Toward increasing our knowledge of the past: A discussion. In G. J. Gumerman & M. Gell-Mann (Eds.), Understanding complexity in the prehistoric southwest (Proceedings, Vol. 16 (Book 16). Santa Fe Institute studies in the sciences of complexity, pp. 149–191). Boulder, CO: Westview Press.

    Google Scholar 

  • Cowgill, G. E. (2000). “Rationality” and contexts in agency theory. In M. A. Dobres & J. E. Robb (Eds.), Agency in archaeology (pp. 51–60). London: Routledge.

    Google Scholar 

  • Crooks, A. T., & Castle, C. J. E. (2012). The integration of agent-based modeling and geographic information for geospatial simulation. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models of geographical systems (pp. 219–252). New York: Springer.

    Chapter  Google Scholar 

  • Cunningham, J. J. (2003). Transcending the “Obnoxious Spectator”: A case for processual pluralism in ethnoarchaeology. Journal of Anthropological Archaeology, 22, 389–410.

    Article  Google Scholar 

  • de Jager, M., Weissing, F. J., Herman, P. M. J., Nolet, B. A., & van de Koppel, J. (2011). Lévy walks evolve through interaction between movement and environmental complexity. Science, 332, 1551–1553.

    Article  Google Scholar 

  • de Jager, M., Weissing, F. J., Herman, P. M. J., Nolet, B. A., & van de Koppel, J. (2012). Response to comment on “Lévy walks evolve through interaction between movement and environmental complexity”. Science, 335, 918d.

    Article  Google Scholar 

  • Doran, J. E. (2000). Trajectories to complexity in artificial societies: Rationality, belief and emotions. In T. A. Kohler & G. J. Gumerman (Eds.), Dynamics in human and primate societies: Agent-based modeling of social and spatial processes (pp. 89–144). New York: Oxford University Press.

    Google Scholar 

  • Doran, J. (2008). Review of “the model-based archaeology of socionatural systems”. Journal of Artificial Societies and Social Simulation, 11, 1–4.

    Google Scholar 

  • Eerkens, J. W., & Lipo, C. P. (2005). Cultural transmission, copying errors, and the generation of variation in material culture and the archaeological record. Journal of Anthropological Archaeology, 24, 316–334.

    Article  Google Scholar 

  • Epstein, J. M. (2006). Generative social science: Studies in agent-based computational modeling. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Evans, A. (2012). Uncertainty and error. In A. J. Heppenstall, A. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models for geographical systems (pp. 309–346). Dordrecht, The Netherlands: Springer.

    Chapter  Google Scholar 

  • Galán, J. M., Izquierdo, L. R., Izquierdo, S. S., Santos, J., del Olmo, R., López-Paredes, A., et al. (2009). Errors and artifacts in agent-based modeling. Journal of Artificial Societies and Social Simulation, 12(1), 1. Retrieved from http://jasss.soc.surrey.ac.uk/12/1/1.html.

    Google Scholar 

  • Gilbert, N. (2008). Agent-based models. London: Sage.

    Google Scholar 

  • Graham, S., & Weingart, S. (2015). The equifinality of archaeological networks: An agent-based exploratory lab approach. Journal of Archaeological Method and Theory, 22, 248–274.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Hodder, I. (1976). Spatial analysis in archaeology. Cambridge, England: Cambridge University Press.

    Google Scholar 

  • Hole, F., & Heizer, F. (1969). An introduction to prehistoric archaeology (2nd ed.). New York: Holt, Rinehart, and Winston.

    Google Scholar 

  • Howey, M. C. L. (2011). Multiple pathways across past landscapes: Circuit theory as a complementary geospatial method to least cost path for modeling past movement. Journal of Archaeological Science, 38, 2523–2535.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Jansen, V. A. A., Mashanova, A., & Petrovskii, S. (2012). Comment on “Lévy walks evolve through interaction between movement and environmental complexity”. Science, 335, 918c.

    Article  Google Scholar 

  • Janssen, M. A. (2009). Understanding artificial Anasazi. Journal of Artificial Societies and Social Simulation, 12(4). Retrieved from http://jasss.soc.surrey.ac.uk/12/4/13.html.

  • Kleijnen, J. P. C. (2005). An overview of the design and analysis of simulation experiments for sensitivity analysis. European Journal of Operational Research, 164, 287–300.

    Article  Google Scholar 

  • Kohler, T. A., & Gumerman, G. J. (2000). Dynamics in human and primate societies: Agent-based modeling of social and spatial processes. Santa Fe Institute, studies in the sciences of complexity. New York: Oxford University Press.

    Google Scholar 

  • Kohler, T. A., & van der Leeuw, S. E. (2007). Introduction. Historical socionatural systems and models. In T. A. Kohler & S. E. van der Leeuw (Eds.), The model-based archaeology of socionatural systems (pp. 1–12). Santa Fe, NM: SAR Press.

    Google Scholar 

  • Krist, F. J. J. (2001). A predictive model of paleo-indian subsistence and settlement. Ph.D. Thesis, Department of Anthropology, Michigan State University.

    Google Scholar 

  • Lake, M. W. (2000). MAGICAL computer simulation of mesolithic foraging. In G. J. Gumerman & T. A. Kohler (Eds.), Dynamics in human and primate societies: Agent-based modeling of social and spatial processes (pp. 107–143). New York: Oxford University Press.

    Google Scholar 

  • Lake, M. W. (2004). Being in a simulacrum: Electronic agency. In A. Gardner (Ed.), Agency uncovered: Archaeological perspectives on social agency, power and being human (pp. 191–209). London: UCL Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Lyman, R. L. (2004). The concept of equifinality in taphonomy. Journal of Taphonomy, 2(1), 15–26.

    Google Scholar 

  • Maguire, D. J. (2005). Towards a GIS platform for spatial analysis and modeling. In D. J. Maguire, M. Batty, & M. F. Goodchild (Eds.), GIS, spatial analysis and modeling. Redlands, CA: ESRI Press.

    Google Scholar 

  • McElhany, P., Steel, E. A., Jensen, D., Avery, K., Yoder, N., Busack, C., et al. (2010). Dealing with uncertainty in ecosystem models: Lessons from a complex salmon model. Ecological Applications, 20, 465–482.

    Article  Google Scholar 

  • Morrison, M. S. (1998). Community and coexistence: Kant’s third analogy of experience. Kant-Studien, 89(3), 257–277.

    Article  Google Scholar 

  • Morrison, M. S., & Morgan, M. S. (1999). Models as mediators: Perspectives on natural and social science. In Ideas in context (p. 52). Cambridge, England: Cambridge University Press.

    Google Scholar 

  • Morrison, M. S. (2015). Reconstructing reality: Models, mathematics, and simulations. New York: Oxford University Press.

    Book  Google Scholar 

  • Murphy, J. T. (2012). Exploring complexity with the Hohokam water management simulation: A middle way for archaeological modeling. Ecological Modelling, 241, 15–29.

    Article  Google Scholar 

  • Ngo, T. A., & See, L. (2012). Calibration and validation of agent-based models of land cover change. In A. J. Heppenstall, A. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models for geographical systems (pp. 181–198). Dordrecht, The Netherlands: Springer.

    Chapter  Google Scholar 

  • 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.

    Google Scholar 

  • Premo, L. (2010). Equifinality and explanation: The role of agent-based modeling in postpositivist archaeology. In A. Costopoulos & M. Lake (Eds.), Simulating change, archaeology into the twenty-first century (Foundations of archaeological inquiry, pp. 28–37). Salt Lake City, UT: The University of Utah Press.

    Google Scholar 

  • Raichlen, D. A., Wood, B. M., Gordon, A. D., Mabulla, A. Z. P., Marlowe, F. W., & Pontzer, H. (2014). Evidence of Lévy walk foraging patterns in human hunter-gatherers. Proceedings of the National Academy of Sciences, 111(2), 728–733.

    Article  Google Scholar 

  • Rathje, W. L., Shanks, M., & Witmore, C. (2013). Archaeology in the making, conversations through a discipline. New York: Routledge.

    Google Scholar 

  • Read, D. W. (1990). The utility of mathematical constructs in building archaeological theory. In A. Voorrips (Ed.), Mathematics and information science in archaeology: A flexible framework (Studies in modern archaeology, Vol. 3, pp. 29–60). Bonn, Germany: Helos.

    Google Scholar 

  • Refsgaard, J. C., van der Sluijs, J. P., Etejberg, A. L., & Vanrollegham, P. A. (2007). Uncertainty in the environmental modeling process—A framework and guidance. Environmental Modeling and Software, 22, 1543–1556.

    Article  Google Scholar 

  • Richardson, K. A. (2003). On the limits of bottom-up computer simulation: Towards a nonlinear modeling culture. In R. H. Sprague (Ed.), Proceedings of the 36th Hawaiian international conference on system sciences (pp. 1–9). Los Alamitos, CA: IEEE Computer Science Press.

    Google Scholar 

  • Saltelli, A. (2005). Global sensitivity analysis: An introduction. In K. M. Hanson & F. M. Hemez (Eds.), Sensitivity analysis of model output (pp. 27–43). Los Alamos, NM: Los Alamos National Laboratory.

    Google Scholar 

  • Shanks, M., & Tilley, C. (1987). Re-constructing archaeology. Cambridge, England: Cambridge University Press.

    Google Scholar 

  • Snowling, S. D., & Kramer, J. R. (2001). Evaluating modeling uncertainty for model selection. Ecological Modelling, 138(1), 17–30.

    Article  Google Scholar 

  • Taylor, M. (2009). What is sensitivity analysis? In What is …? series. Hayward Medical Communications, a division of Hayward Group Ltd. Retrieved from www.whatisseries.co.uk Accessed June 13, 2012.

  • van der Sluijs, J. P., Risbey, J., Kloprogge, P., Ravetz, J. R., Funtowicz, S. O., Quintana, S. C., et al. (2003). RIVM/MNP guidance for uncertainty assessment and communication. Retrieved from http://www.nusap.net/downloads/detailedguidance.pdf.

  • 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.

    Article  Google Scholar 

  • van Groenendaal, W. J. H., & Kleijnen, J. P. C. (1997). On the assessment of economic risk: Factorial design versus Monte Carlo methods. Journal of Reliability Engineering and System Safety, 57, 91–102.

    Article  Google Scholar 

  • van Groenendaal, W. J. H., & Kleijnen, J. P. C. (2002). Deterministic versus stochastic sensitivity analysis in investment problems: An environmental case study. European Journal of Operational Research, 141, 8–20.

    Article  Google Scholar 

  • Verhagen, P. (2007). Case studies in archaeological predictive modeling (p. 14). Leiden, The Netherlands: Archaeological Studies Leiden University.

    Book  Google Scholar 

  • Verhagen, P., & Whitley, T. G. (2012). Integrating archaeological theory and predictive modeling: A live report from the scene. Journal of Archaeological Method Theory, 19, 49–100.

    Article  Google Scholar 

  • Viswanathan, G. M., da Luz, M. G. E., Raposo, E. P., & Stanley, H. E. (2011). The physics of foraging. An introduction to random searches and biological encounters. Cambridge, England: Cambridge University Press.

    Book  Google Scholar 

  • Viswanathan, G. M., Buldyrev, S. V., Havlin, S., da Luz, M. G. E., Raposo, E. P., & Stanley, H. E. (1999). Optimizing the success of random searches. Nature, 401, 911–914.

    Article  Google Scholar 

  • Vonk Noordegraaf, A., Nielen, M., & Kleijnen, J. P. C. (2003). Sensitivity analysis by experimental design. European Journal of Operational Research, 146, 433–443.

    Article  Google Scholar 

  • von Bertalanffy, L. (1949). Problems of organic growth. Nature, 163, 156–158.

    Article  Google Scholar 

  • von Bertalanffy, L. (1956). General system theory. General Systems, 1, 1–10.

    Google Scholar 

  • Westervelt, J. D. (2002). Geographic information systems and agent-based modeling. In H. R. Gimblett (Ed.), Integrating geographic information systems and agent-based modeling techniques for simulating social and ecological processes (pp. 83–104). Oxford, England: Oxford University Press.

    Google Scholar 

  • 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.

    Google Scholar 

  • Wurzer, G., Kowarik, K., & Reschreiter, H. (2015). Agent-based modeling and simulation in archaeology (Advances in geographic information science). Vienna, Austria: Springer.

    Book  Google Scholar 

  • Zeigler, B. P. (1976). Theory of modeling and simulation. New York: Wiley.

    Google Scholar 

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Authors and Affiliations

  1. Department of Anthropology, University of New Hampshire, Durham, NH, USA

    Marieka Brouwer Burg

  2. Groningen Institute of Archaeology, University of Groningen, Groningen, The Netherlands

    Hans Peeters

  3. Department of Anthropology and MSU Museum, Michigan State University, East Lansing, MI, USA

    William A. Lovis

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  1. Marieka Brouwer Burg
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  2. Hans Peeters
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Correspondence to Marieka Brouwer Burg .

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  1. Department of Anthropology, University of New Hampshire, Durham, New Hampshire, USA

    Marieka Brouwer Burg

  2. Groningen Institute of Archaeology, University of Groningen, Groningen, The Netherlands

    Hans Peeters

  3. Department of Anthropology and MSU Muse, Michigan State University, East Lansing, Michigan, USA

    William A. Lovis

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Brouwer Burg, M., Peeters, H., Lovis, W.A. (2016). Introduction to Uncertainty and Sensitivity Analysis in Archaeological Computational Modeling. 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_1

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