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Scale Dependency in Agent-Based Modeling: How Many Time Steps? How Many Simulations? How Many Agents?

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Uncertainty and Sensitivity Analysis in Archaeological Computational Modeling

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

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Abstract

As with other parameters a modeler might sweep and document in a sensitivity analysis, it is important to explore the scale dependency of a model. Decisions about how long to run a simulation to generate patterns of interest, or how many simulations are necessary to capture the range of patterns generated by a stochastic model, are an important part of the design and testing process. In archaeological agent-based modeling (ABM), though, researchers have only recently begun to approach these issues systematically. More often, pragmatic concerns related to the time required to run simulations have determined scaling rather than a quantitative assessment of the often diminishing marginal returns of adding one more agent to the simulation or one more simulation to the analysis. Documenting the scale sensitivity of a model can help researchers better manage their time and resources. My ABM project on the organization of the Hohokam economy in central Arizona (AD 200–1450) involved a program of systematically exploring the sensitivity of simulation models to scale-dependent parameters. The research has contributed new insights into the Hohokam pottery distribution system, particularly related to the emergence and organization of a nascent market-based economy.

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Notes

  1. 1.

    Estimating populations for the Hohokam, particularly during the Preclassic, is notoriously difficult and attempts have generated widely varying results. Site sizes, room counts, and the use-life of pit houses in the Hohokam culture area are exceedingly difficult to assess with any accuracy. In my opinion, the work of Doelle (1995, 2000), Craig et al. (2012), and others working from similar room or house count data probably underestimates the actual population of the greater Phoenix Basin. But regardless of the real numbers, the general trends drawn from Doelle (1995, 2000) and updated by Matthew Peeples and the Arizona State University Biocomplexity Project (Nelson et al. 2010) likely capture the general trajectory of Hohokam population growth through time.

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Acknowledgments

Parts of this chapter were adapted from my dissertation, and I appreciate the input on that document from my committee, including David Abbott (chair), Michael Barton, Marco Janssen, and Sander van der Leeuw. Michael Barton, Sean Bergin, and Wendy Cegielski helped with the initial brainstorming of the topic, and Kristin Gade had to suffer through my attempts to render the main argument. Note that the testing and experiments described here were conducted on computers purchased with the aid of an award from the Society for American Archaeology (2012 Fred Plog Memorial Fellowship).

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Authors and Affiliations

  1. Center for Social Dynamics and Complexity, Arizona State University, Tempe, AZ, USA

    Joshua Watts

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  1. Joshua Watts
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Correspondence to Joshua Watts .

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Editors and Affiliations

  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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Watts, J. (2016). Scale Dependency in Agent-Based Modeling: How Many Time Steps? How Many Simulations? How Many Agents?. 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_6

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  • DOI: https://doi.org/10.1007/978-3-319-27833-9_6

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  • Online ISBN: 978-3-319-27833-9

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