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Simulating Prehistoric and Ancient Worlds (Computational Social Sciences)

Barceló, Juan A. and Castillo, Florencia Del (eds.)
Springer-Verlag: Berlin, 2016
ISBN 9783319314815 (pb)

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Reviewed by Stefani Crabtree
Pennsylvania State University

Cover of book The book “Simulating Prehistoric and Ancient Worlds” edited by Juan Barceló and Florencia del Castillo is not simply a tidy collection of archaeological simulations. It offers a synthesis of agent-based modeling to date, provides multiple well-developed case studies, and contributes to the literature on formal models in archaeology by demonstrating the utility of simulation.

While many edited volumes start with a brief introduction of the motivation for the volume, Barceló and del Castillo take the opportunity to provide a lengthy summary of agent-based modeling in social systems up to the point of this book’s writing. The first chapter, a thorough 140 pages long, is highly useful for those wishing to introduce themselves to complexity science and its applications in anthropology. New students to agent-based models (and complexity science) would be wise to mine this chapter for both an introduction to the field as it stands, and for references to help them along their way in their research. Those who have already been using modeling approaches in their work will also find this chapter instrumental as a synthesis of the field to date, providing a useful encyclopedia. Saqalli and Baum in Chapter 8 further advance the discussion of agent-based models and offer almost a step-by-step guide for using ABMs to understand landscape patterns, and readers who focus on Chapter 1 would be wise to also look to Chapter 8 for discussions on landscape.

The first few chapters of the book explore how environmental composition can influence complex processes, such as migration events. Timm et al.’s chapter (Chapter 2) provides a thorough examination of the physical environment and how that could influence hominin dispersal. Their chapter is especially useful for understanding how to think through theories and how to test them in a mechanistic fashion. Janssen and Hill’s model (Chapter 3), built on an optimal foraging theory framework, examines how group demographics influence foraging returns. Their work demonstrates the utility of using well-developed theories to build models of real systems. Oestmo et al. further explore environmental composition in Chapter 4 by re-examining Brantingham’s (2003) model of raw material procurement. The physical location of these resources has implications for forager movements, and may suggest the development of different strategies (e.g., embedded procurement) by real foragers. Fort et al., in a slightly different tack in Chapter 5, examine how the filling of the landscape affects rate of migration. Their simple approach of modeling a wave-of-advance is important for understanding how hominins have spread to the four-corners of the globe.

O’Brien and Burgh in Chapter 6 combine agent-based modeling and least cost path (LCP) analysis, an ever-growing interest in anthropological studies. Finding prehistoric pathways may help us understand important characteristics of human migration, demonstrated recently by Frachetti et al. (2017), but also in the state-of-the-art work by White and Barber (2012). A further use of “growing” a landscape in ABM is demonstrated in Baum’s work in Chapter 9. Of interest is Baum’s discussion of validation, a sticky topic in modeling, and this work provides a useful caution for validating our models.

Isern and Fort’s work (Chapter 7) is interesting, as it approaches a more intangible concept—culture shift—which is not usually the purview of archaeologists who have tended to focus more on physical shifts. Their work is novel and insightful, both for demonstrating the power of computational models to approach diverse ideas, but also the utility of their approach for understanding language shift. Related to this work, Chapter 10 by Sakahira and Terano examines the onset of rice-based agriculture during the Yayoi period and whether (or not) Jomon groups assimilated to agriculture. These two cases compliment each other and should be of interest to any researchers studying population expansions.

In Chapter 11 Matsumoto and Sasakura further examine the Jomon-Yayoi transitional period, focusing on the demographic changes of the two populations that lived during this time. It is interesting to have two models approaching this research question back-to-back in one volume, and this demonstrates the utility of multi-faceted approaches and plurality of opinions in understanding science.

Štekerová and Danielisová model the transition during the Iron Age of populations in Central Europe to living in highly aggregated oppida. They intrinsically link their model to carrying capacity, a well-documented limiter to population growth (Janssen, 2009). Their model is a good first-step in understanding population dynamics during the early Iron Age in Europe, a period that has not been heavily modeled (though see Crabtree, 2016) yet has a rich archaeological history that would benefit from the formal hypothesis testing that ABMs can offer.

Bogle and Cioffi-Revilla examine the development of polities on the Zambezi Plateau in southern Africa. Their work adds to a growing body of work that looks at how people group into complex polities, and how those polities dissolve (Crabtree, Bocinsky, Hooper, Ryan, & Kohler, 2017; Turchin & Gavrilets, 2009). Understanding how and why small, self-sufficient, egalitarian communities will circumscribe themselves into a hierarchy within which they are not the leaders is a puzzle; ABMs, like the one in Chapter 12, can help address these complex ideas.

Finally, Trescak, Bogdanovych and Simoff attack a problem that most modelers have had asked of them: what about heterogenous agents who learn, and who don’t all have the exact same strategies? This approach should be adopted in future models by other modelers, as it has the potential to address challenges that all modelers face, by trying to understand diverse populations via a simple combination of rules.

As agent-based modeling grows in archaeology, and as students seek to replicate models for class projects, this volume will become dog-eared and well loved by professors and students alike. From Barceló and del Castillo’s encyclopedic introduction, to each of the well-developed case studies, this volume adds to a growing field and will be useful for archaeologists and computer scientists interested in prehistory.

* References

BRANTINGHAM, P. (2003). A Neutral Model of Stone Raw Material Procurement. American Antiquity, 68(3), 487–509.

CRABTREE, S. A. (2016). Simulating Littoral Trade: Modeling the Trade of Wine in the Bronze to Iron Age Transition in Southern France. Land, 5(1), 5. https://doi.org/10.3390/land5010005

CRABTREE, S. A., Bocinsky, R. K., Hooper, P. L., Ryan, S. C., & Kohler, T. A. (2017). How to Make a Polity (in the central Mesa Verde). American Antiquity 82(1), 71-95.

FRACHETTI, M. D., Smith, C. E., Traub, C. M., & Williams, T. (2017). Nomadic ecology shaped the highland geography of Asia’s Silk Roads. Nature, 543(7644), 193–198. Retrieved from http://dx.doi.org/10.1038/nature21696

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

TURCHIN, P., & Gavrilets, S. (2009). The Evolution of Complex Hierarchical Societies. Social Evolution and History, 8, 167–198.

WHITE, D. A., & Barber, S. B. (2012). Geospatial modeling of pedestrian transportation networks: a case study from precolumbian Oaxaca, Mexico. Journal of Archaeological Science, 39(8), 2684–2696. doi:10.1016/j.jas.2012.04.017


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