Abstract
The absence of sensitivity analysis (SA) in archaeological predictive modeling, and indeed in contemporary archaeological research design generally, places the discipline in stark contrast to other disciplines routinely employing such approaches. The lack of routinized use of SA diminishes credibility of model outcomes, and without it, loci of model uncertainty remain undetermined—whether attributable to parameterization or other model elements. This essay explores the nature of archaeological modeling goals, the constraints of archaeological data and the impact on uncertainty, and, finally, the potential position and role of SA in archaeological research design. In particular, issues of the tandem application of environmental and sociobehavioral modeling are addressed. It is argued that simplification of both models and expectations may well result in enhanced ability to effectively employ SA in archaeological modeling enterprises, and bolster outcome confidence.
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Notes
- 1.
For my purposes, here I will ignore the various limitations of machine computing addressed by Evans (2012) as they might impact error and uncertainty.
- 2.
This distinction to a degree replicates a long-standing debate in evolutionary theory between the primacy of individual and group selection (i.e., Darwin vs. Wynn Edwards; Borello 2005), as well as the role of information and its potentially differential distribution and access within the system (Whallon 2006, 2011). This distinction has also been recognized by Doran (1999) in his evaluation of “Agent Based Modeling in Archaeology,” and the differences between what he terms “individual cognition” and “group cognition,” although there are nuances of this distinction that cannot be afforded space here. Likewise, they may be variable in scale, being broadly applicable or narrowly so; the latter keyed to the individual case rather than the broader arena of like cases (recognizing that even simulation analyses keyed to individual cases may explain more than the case of interest).
References
Andres, T. (2010). Sensitivity analysis. In N. J. Salkind (Ed.), Encyclopedia of research design (pp. 1340–1341). Thousand Oaks, CA: Sage.
Anonymous. (2013). A bone to pick. The Economist, Dec 21, 2013. Retrieved February 10, 2016 from http://www.economist.com/news/science-andtechnology/21591837-enthusiastic-amateur-suggests-work-how-dinosaurs-grew-wrong-bone
Baxter, M. J. (1994). Exploratory multivariate analysis in archaeology. Edinburgh, England: Edinburgh University Press.
Beekman, C. S., & Baden, W. W. (Eds.). (2005). Non-linear models for archaeology and anthropology. Burlington, VT: Ashgate.
Bernard, R. N. (1999). Using adaptive agent-based simulation models to assist planners in policy development: The case of rent control. Working paper 99-07-052. Santa Fe, NM: Santa Fe Institute.
Bevan, A., & Wilson, A. (2013). Models of settlement hierarchy based on partial evidence. Journal of Archaeological Science, 40, 2415–2427.
Binford, L. R. (1978). Nunamiut ethnoarchaeology. New York: Academic.
Borello, M. (2005). The rise, fall and resurrection of group selection. Endeavour, 29(1), 43–47.
Brouwer Burg, M. (2016). GIS-based modeling of archaeological dynamics: Strengths, weaknesses, and the utility of sensitivity analysis. In Brouwer Burg, H. Peeters, & W. A. Lovis (Eds.), Uncertainty and sensitivity in archaeological computational modeling. New York: Springer.
Carr, C. (1985a). Perspective and basic definitions. In C. Carr (Ed.), For concordance in archaeological analysis, bridging data structure, quantitative technique and theory (pp. 1–17). Prospect Heights, NY: Waveland Press.
Carr, C. (1985b). Getting into data: Philosophy and tactics for the analysis of complex data structures. In C. Carr (Ed.), For concordance in archaeological analysis, bridging data structure, quantitative technique and theory (pp. 18–44). Prospect Heights, NY: Waveland Press.
Carroll, J. (2016). In M. Brouwer Burg, H. Peeters, & W. A. Lovis (Eds.), Uncertainty and sensitivity in archaeological computational modeling. New York: Springer.
Clarke, D. L. (1970). Models in archaeology. London: Methuen.
Costopoulos, A., & Lake, M. W. (Eds.). (2010). Simulating change: Archaeology into the twenty-first century, foundations of archaeological inquiry. Salt Lake City, UT: University of Utah Press.
Costopoulos, A., Lake, M. W., & Gupta, N. (2010). Introduction. In A. Costopoulos & M. Lake (Eds.), Simulating change: Archaeology into the twenty-first century (foundations of archaeological inquiry) (pp. 1–8). Salt Lake City, UT: University of Utah Press.
Crooks, A. T., & Heppenstall, A. J. (2012). Introduction to agent-based modeling. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models of geographical systems (pp. 85–108). New York: Springer.
Doran, J. (1970). Systems theory, computer simulations, and archaeology. Special issue: Analysis. World Archaeology, 1(3), 289–298.
Doran, J. (1999). Prospects for agent based modelling in archaeology. Archeologia e Calcolatori, 10, 33–44.
Doran, J. (2008). Review of: The model based archaeology of socionatural systems. In T. A. Kohler & S. E. van der Leeuw (Eds.), Socializing complexity: Approaches to power and interaction in archaeological discourse. In S. Kohring & S. Wyne-Jones (Eds.), Journal of Artificial Societies and Social Simulation. Retrieved March 19, 2014, from http://jasss.soc.surrey.ac.uk/11/3/reviews/doran.html.
Doran, J. (2011). Review of: Simulating change: Archaeology into the twenty-first century (foundations of archaeological inquiry). In A. Costopoulos & M. W. Lake (Eds.), Journal of Artificial Societies and Social Simulation. Retrieved December 02, 2014, from http://jasss.soc.surrey.ac.uk/14/4/reviews/2.html.
Evans, A. (2012). Uncertainty and error. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models of geographical systems (pp. 309–346). New York: Springer.
Fayyad, U., Piatetsky-Shapiro, G., & Padhraic, S. (1996). From data mining to knowledge discovery in databases. Journal of Artificial Intelligence, 11(3), 37–53.
Gilbert, N. (2008). Agent based models. Thousand Oaks, CA: Sage Research Methods.
Hamby, D. M. (1994). A review of techniques for parameter sensitivity analysis of environmental models. Environmental Monitoring and Assessment, 32, 135–154.
Hegmon, M. (2003). Setting theoretical egos aside: Issues and theory in North American archaeology. American Antiquity, 68, 213–243.
Henrickson, L., & McKelvey, B. (2002). Foundations of “New” social science: Institutional legitimacy from philosophy, complexity science, postmodernism, and agent-based modeling. Proceedings of the National Academy of Sciences, 99(Suppl. 3), 7288–7295.
Heppenstall, A. J., Crooks, A. T., See, L. M., & Batty, M. (Eds.). (2012). Agent-based models of geographical systems. New York: Springer.
Ioannidis, J. P. (2005). Why most published research findings are false. PLOS Medicine. doi:10.1371/journal.pmed.0020124.
Jankowski, P., Andrienko, N., & Andrienko, G. (2010). Map centered exploratory approach to multiple criteria spatial decision making. International Journal of Geographical Information Science, 15(2), 101–127.
Jochim, M. (1976). Hunter-gatherer subsistence and settlement: A predictive model. New York: Academic.
Kurzer, G., Kowarik, K., & Reschreiter, H. (Eds.). (2015). Agent based modeling and simulation in archaeology. New York: Springer.
Lake, M. W. (2014). Trends in archaeological simulation. Journal of Archaeological Method and Theory, 21, 258–278.
Lovis, W. A., Arbogast, A. F., & Monaghan, G. W. (2012a). The geoarchaeology of Lake Michigan Coastal Dunes. Environmental research series no. 2. East Lansing, MI: Michigan Department of Transportation, Michigan State University Press.
Lovis, W. A., Monaghan, G. W., Arbogast, A. F., & Forman, S. L. (2012a). Differential temporal and spatial preservation of archaeological sites in a great lakes coastal zone. American Antiquity, 77(3), 591–608.
Merton, R. K. (1949). On sociological theories of the middle range. In R. K. Merton (Ed.), Social theory and social structure (pp. 39–53). New York: Simon & Schuster/The Free Press.
Monaghan, G. W., Arbogast, A. F., Lovis, W. A., & Kowalski, D. (2013). Millennial-scale cycles of coastal dune formation during the Late Holocene, Lake Michigan. North-Central GSA Section Meeting. Abstracts with Programs, 45(4), 67.
Myhrvold, N. P. (2013). Revisiting the estimation of dinosaur growth rates. PLOS ONE, 16, 2013. doi:10.1371/journal.pone.0081917.
Ngo, T. A., & See, L. M. (2012). Calibration and validation of agent-based models of landcover change. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models of geographical systems (pp. 181–198). New York: Springer.
Peeters, H., & Romeijn, J.-W. (2016). Uncertainty in exploratory computational modeling in archaeology: A case study between theory and practice. In M. Brouwer Burg, H. Peeters, & W. A. Lovis (Eds.), Uncertainty and sensitivity in archaeological computational modeling. New York: Springer.
Premo, L. S. (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: University of Utah Press.
Raab, L. M., & Goodyear, A. C. (1984). Middle-range theory in archaeology: A critical review of origins and applications. American Antiquity, 49, 255–268.
Renfrew, C. (1981). The simulator as demiurge. In J. Sabloff (Ed.), Simulations in archaeology (pp. 283–306). Albuquerque, NM: The University of New Mexico Press.
Rogers, E. (1962). The Round Lake Ojibwa. Occasional paper 5. Toronto, ON: Royal Ontario Museum, University of Toronto.
Schiffer, M. B. (1976). Behavioral archaeology. New York: Academic.
Stanilov, K. (2012). Space in agent-based models. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models of geographical systems (pp. 253–269). New York: Springer.
Verhagen, P., & Whitley, T. G. (2012). Integrating archaeological theory and predictive modeling: A live report from the scene. Journal of Archaeological Method and Theory, 19, 49–100.
Whallon, R. (2006). Social networks and information: Non-“utilitarian” mobility among hunter-gatherers. Journal of Anthropological Archaeology, 25, 259–270.
Whallon, R. (2011). An introduction to information and its role in hunter gatherer bands. In R. Whallon, W. A. Lovis, & R. K. Hitchcock (Eds.), The role of information in hunter-gatherer bands (Ideas, debates and perspectives 5, pp. 1–28). Los Angeles, CA: Cotsen Institute of Archaeology, University of California.
White, A. (2016). In M. Brouwer Burg, H. Peeters, & W. A. Lovis (Eds.), Uncertainty and sensitivity in archaeological computational modeling. New York: Springer.
Acknowledgments
I owe deep thanks to Kyle Bocinsky for his insightful comments into an earlier draft of this chapter, and for pointing me to some critical literature. Henk Weerts was the catalyst that prodded our research group to collectively consider the application of sensitivity analysis to archaeological modeling. Jim Doran provided multiple thought provoking observations that resulted in a much better product, but likely still with flaws of my own making and responsibility.
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Lovis, W.A. (2016). Is There a Research Design Role for Sensitivity Analysis (SA) in Archaeological 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_2
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