While working on the Agent-based Model Simulation of the Trading along the Ancient Silk Route, one of the prominent models that inspired me was the Project MERCURY-SIMREC. For the CSS 610: Agent-based Modeling Simulation / Agent-based Modeling Economics course, we were to demonstrate an ABM we find interesting in class, and here are my notes and presentations from the class. For the Fall 2020 semester, I have a course, CSS 620: Origins of Social Complexity which is described as a study of human societies, specifically Meso-America, West Asia, East Asia, and Andean Peru. This is an attempt to review, overview, understand the creation, and process of the Project MERCURY-SIMREC model based on Ancient Roman civilizations.
Agent-based Models in Archaeology
“…untangle the complexities of past human behaviour: computational modelling… focus on individual phenomena like trade, individual preference or political regulation… identify what evidence they would find if their theory was true… test hypotheses related to their theories by comparing simulated archaeology with real excavated archaeology.”Project MERCURY-SIMREC
Background and Motivation
- I have a vested interest in Digital Humanities research, since it combines application of technology to the study of humanities and social sciences. Digital and Computational History is a fast evolving field within the Digital Humanities.
- In a Computational History course, I came across several spatial visualizations of digital history projects and realized the potential of agent-based models is enormous in the field. They contribute to realizing the possibilities of the past and human civilizations as a whole while also being visual representations of that world.
- Oxymoronic as it sounds, Predictive History is a rich field of Digital History with possibilities. Considering the unavailability of data and lack of written records in a cohesive manner, there are vast periods of time in history which are not widely known, which can be remedied with interactive understanding of these models. It might lead to better understanding of socio-economic-political-cultural-civilization growth and effects, helping us understand our past better.
- ABMs act as a “trace-forward-to-the-present” detective-mystery story, where the present is the endpoint, the available data act as clues, and the mystery is the entirety of the past occurrences narrowed into the form of a research question. Agent based modeling is a tool for discovery to humanize the people of yesteryear and make their lived reality understandable.
- I am working on a degree-end ABM research project on historic Silk Routes. (HERMES: Historic Economic Redistribution in Markets with Eurasian Spices & Silks) and would like to implement the ideas from Project MERCURY to this model.
- I would like to explore this space as a future research possibility, especially for the Global South historical studies, perhaps for my doctoral studies.
Project MERCURY-SIMREC: Computational Modelling in Roman Economy Studies
- Marie-Curie Actions Individual Fellowship (European Union’s Horizon 2020 programme)
- Leverhulme Trust
Headed by Tom Brughmans from the University of Oxford’s School of Archaeology
Project MERCURY Model Library
Project MERCURY has the following phenomena modeled:
- Centurions in the Roman legion (Xavier Rubio-Campillo, with Valdés Matías, P. and Ble, E.) in C++.
- Demography dynamics and army recruitment in the Dutch limes (Verhagen P, J Joyce & M Groenhuijzen) in NetLogo.
- MERCURY: an ABM of tableware trade in the Roman East, extended to include transport-cost and population dynamics (Tom Brughmans & Jeroen Poblome) in NetLogo.
- ORBIS: importing a Roman transport system (Code and .graphml representation by Tom Brughmans. ORBIS data by Walter Scheidel, Elijah Meeks & Karl Grossner) in NetLogo.
- Modeling the Roman Bazaar with social networks (Shawn Graham & Scott Weingart) in NetLogo.
- Social learning amphorae (Coto-Sarmiento & Rubio-Campillo) in R.
- Spatial transportation network models of Roman sites in Netherlands (Groenhuijsen & Verhagen) in Python.
Project MERCURY is also working on case studies on Cultural Transmission, Amphorae reuse, Product preferences, road and urban networks in ancient Roman civilization.
The biggest advantage of, or element that works in favor for the project is the availability of open data.
MERCURY: Market Economy and Roman Ceramics Redistribution
A trade map from the ancient Roman empire is shown below. If we were to focus on the Eastern Mediterranean side of the empire, a curious pattern emerges in terms of the popularity and distribution of four types of ceramic tableware known as the Eastern Sigillata – A, B, C, and D.
Many historians and archaeologists have been debating and discussing the rise in popularity of Eastern Sigillata A as well as its decline in popularity. It has been theorized that this is due to its redistribution across the empire, across a period of 25 years.
An ABM can be constructed to trace the trade of the tableware, and their redistribution over a trade cycle, possibly simulating the demand and thus the quantity of the tableware A in the regions.
Conceptual Economic Models
There are several theories for historical Roman trade. Many use the following as the main parameters.
- State involvement
- Re-distributive centers consumption “pulling forces”
- Commercial “piggy-back” trade
- Closeness to large-scale agricultural production
For the Project MERCURY model, the focus is on information flow between traders in local and wider networks. The following two theories are carefully considered:
Peter Bang’s Roman bazaar theory (2008)
- Local markets had high uncertainty of information.
- ‘Small-world’ social networks between traders.
- Trader communities are opportunistic & protectionist.
Peter Temin’s Roman market economy theory (2013)
- Strongly connected, well-integrated markets
- Higher degree of alternative market integration
- Flow of commercial information between traders contributes to the Market Integration
The Roman Bazaar theory is conceptualized as the Null Hypothesis to build the ABM, which eventually even becomes a Monopoly-style board game called FORVM
Limited integration of markets proposed by Bang’s model is highly unlikely. Importance of market integration as suggested by Temin’s economic model more likely. In the case of the redistribution iteration of the item, of the Eastern Sigillata A Tableware reduces in demand and popularity over a period of time in the model, just as in the archaeological findings.
Why did the distribution between the tableware shift?
- Differences in the potential production output of tableware production centers.
- Demand from traders of their local markets.
- The Project MERCURY Redistribution of Roman Tableware Model can be found here: https://www.comses.net/codebases/4347/releases/1.1.0/
- The other models from Project MERCURY are available here: https://projectmercury.eu/model-library/
- In the case of ORBIS: importing a Roman transport system especially, a NetLogo Network extension must be installed in the exact same directory as the model and the NetLogo installation itself. The network extension can be found here: http://ccl.northwestern.edu/netlogo/5.0/docs/nw.html
- Note that the models run only in the NetLogo versions they have been built in, unless modified manually to be compatible with newer versions of NetLogo.
Note that the featured image for this blog post is the wall from a building at a “Ad Cucumas” shop in Herculaneum, Italy, with an advertisement for four wine jars (cucumae) of different colors and prices.
Yes, yes, I call it “Hermes”, like the Greek God of Travel, Commerce, Trade, etc., but “Her mess” works too, at this point. The model is still in construction phase but Project MERCURY inspired me beyond even the naming of the model. I figured out how to apply research in a historical economical model, from Project MERCURY. The research paper details the steps in the process. Above all, the possibility of such a study was absolutely influential in me pursuing the large scale exploration of trading along the Ancient Silk Route.
References & Further Reading on ABMs in Archaeology
Bollen, K. A., Gilbert, N., & Doran, J. (1995). Simulating Societies: The Computer Simulation of Social Phenomena. Social Forces, 74(2), 745. https://doi.org/10.2307/2580509
Christopher Stockard Beekman, & Baden, W. W. (2016). Nonlinear models for archaeology and anthropology continuing the revolution. London New York Routledge.
Gumerman, G. J., Swedlund, A. C., Dean, J. S., & Epstein, J. M. (2003). The Evolution of Social Behavior in the Prehistoric American Southwest. Artificial Life, 9(4), 435–444. https://doi.org/10.1162/106454603322694861
Kirch, P. V. (2010). Questioning Collapse: Human Resilience, Ecological Vulnerability, and the Aftermath of Empire. Patricia A. McAnany , Norman Yoffee. Journal of Anthropological Research, 66(4), 555–556. https://doi.org/10.1086/jar.66.4.20798880
Kohler, T. A., Gumerman, G. J., & Reynolds, R. G. (2005). Simulating Ancient Societies. Scientific American, 293(1), 76–84. https://doi.org/10.1038/scientificamerican0705-76
Tainter, J. A. (2017). The collapse of complex societies. Cambridge University Press.
Wurzer, G., Kowarik, K., & Reschreiter, H. (2016). Agent-based modeling and simulation in archaeology. Springer.
Abadie-Reynal, C. 1989. Céramique et commerce dans le bassin Egéen du IVe au VIIe siecle, in Kravari, V., Lefort, J. & Morrisson, C. (ed.) Hommes et richesses dans l’empire byzantin. Tome I, IVe-VIIe siècle: 143–59. Paris: Éditions P. Lethielleux.
Bang, P.F. 2008. The Roman bazaar, a comparative study of trade and markets in a tributary empire. Cambridge: Cambridge University Press.
Bes, P. 2015. Once upon a time in the East. The chronological and geographical distribution of Terra Sigillata and Red Slip Ware in the Roman East (Roman and Late Antique Mediterranean Pottery 6). Oxford: Archaeopress.
Bes, P. & Poblome, J.. 2008. (Not) see the wood for the trees? 19,700+ sherds of sigillata and what we can do with them. . . Rei Cretariae Romanae Fautorum Acta 40: 505–14. Bonn: Dr Rudolf Habelt.
Brughmans, T., & Poblome, J. (2016a). MERCURY: an agent-based model of tableware trade in the Roman East. Journal of Artificial Societies and Social Simulation, 19(1), http://jasss.soc.surrey.ac.uk/19/1/3.html.
Brughmans, T., & Poblome, J. (2016b). Roman bazaar or market economy? Explaining tableware distributions through computational modelling. Antiquity, 90(350), 393–408. https://doi.org/10.15184/aqy.2016.35
Brughmans, T., & Poblome, J. (2017). The case for computational modelling of the Roman economy: a reply to Van Oyen. Antiquity, 91(359), 1364–1366. https://doi.org/10.15184/aqy.2017.166
Conrad Djurdjevac, Nataša, Daniel, C., Ana, F., Martin, H., Wolfram, P., Brigitta, S., … Johannes, W. (2018). Mathematical Modeling of the Spreading of Innovations in the Ancient World. ETopoi. Journal for Ancient Studies, 7, 1–32. https://doi.org/10.17171/4-7-1
Conrad Djurdjevac, Natasa, Helfmann, L., Zonker, J., Winkelmann, S., & Schütte, C. (2017). Human mobility and innovation spreading in ancient times: A stochastic agent-based simulation approach. European Physics Journal.
Conrad Djurdjevac, Nataša, Helfmann, L., Zonker, J., Winkelmann, S., & Schütte, C. (2018). Human mobility and innovation spreading in ancient times: a stochastic agent-based simulation approach. EPJ Data Science, 7(24). https://doi.org/10.1140/epjds/s13688-018-0153-9
Coto-sarmiento, M., Rubio-campillo, X., & Remesal, J. (2018). Identifying social learning between Roman amphorae workshops through morphometric similarity. Journal of Archaeological Science, 96(April), 117–123. https://doi.org/10.1016/j.jas.2018.06.002
Crabtree, S. (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
Fousek, J., Kaše, V., Mertel, A., Výtvarová, E., & Chalupa, A. (2018). Spatial constraints on the diffusion of religious innovations: The case of early Christianity in the Roman Empire. PLOS ONE, 13(12), e0208744. Retrieved from https://doi.org/10.1371/journal.pone.0208744
Fousek, J., Výtvarová, E., Mertel, A., Chalupa, A., & Hladká, E. (2016). Agent-Based Modelling And Simulation For The Geospatial Network Model Of The Roman World. In International Symposium on Grids and Clouds (ISGC) 2016.
Graham, S. (2006). Networks, Agent-Based Models and the Antonine Itineraries: Implications for Roman Archaeology. Journal of Mediterranean Archaeology, 19(1), 45–64. https://doi.org/10.1558/jmea.2006.19.1.45
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. https://doi.org/10.1007/s10816-014-9230-y
Groenhuijzen, M. R., & Verhagen, P. (2016). Testing the Robustness of Local Network Metrics in Research on Archeological Local Transport Networks. Frontiers in Digital Humanities, 3(6), 1–14. https://doi.org/10.3389/fdigh.2016.00006
Groenhuijzen, M. R., & Verhagen, P. (2017). Comparing network construction techniques in the context of local transport networks in the Dutch part of the Roman limes. Journal of Archaeological Science: Reports, 15, 235–251. https://doi.org/10.1016/j.jasrep.2017.07.024
Joyce, J., & Verhagen, P. (2016). Simulating the Farm: Computational Modelling of Cattle and Sheep Herd Dynamics for the Analysis of Past Animal Husbandry Practices. LAC 2014 Proceedings, 0(0), 17. https://doi.org/10.5463/lac.2014.59
Komoróczy, B., & Vlach, M. (2015). Simulating archeological models: Perspectives in protohistory. In S. Sázelová, M. Novák, & A. Mizerová (Eds.), Forgotten times and spaces: New perspectives in paleo-anthropological, paleontological and archeological studies. 1st Edition. (pp. 494–506). Brno: Institute of Archeology of the Czech Academy of Sciences; Masaryk University. https://doi.org/10.5817/CZ.MUNI.M210
Rubio-Campillo, X., Matías, P. V., & Ble, E. (2015). Centurions in the Roman Legion: Computer Simulation and Complex Systems. Journal of Interdisciplinary History, 46(2), 245–263. https://doi.org/10.1162/JINH_a_00833
Snyder, J. R., Dilaver, O., Stephenson, L. C., Mackie, J. E., & Smith, S. D. (2018). Agent-based modelling and construction – reconstructing antiquity’s largest infrastructure project. Construction Management and Economics, 36(6), 313–327. https://doi.org/10.1080/01446193.2017.1403639
Temin, P. (2009). The Roman Bazaar: A Comparative Study of Trade and Markets in a Tributary Empire. By Peter Fibiger Bang. Cambridge: Cambridge University Press, 2008. Pp. xv, 358. $110. The Journal Of Economic History, 69(4), 1165-1166. doi: 10.1017/s0022050709001478
Van Oyen, A. (2017). Agents and commodities: a response to Brughmans and Poblome (2016) on modelling the Roman economy. Antiquity, 91(359), 1356–1363. https://doi.org/10.15184/aqy.2017.138
Verhagen, P., Joyce, J., & Groenhuijzen, M. R. (2019). Finding the Limits of the Limes: modelling demography, economy and transport on the edge of the Roman Empire. Springer. https://doi.org/10.1007/978-3-030-04576-0_1
Verhagen, P., Joyce, J., & Groenhuizen, M. (2016). Modelling the Dynamics of Demography in the Dutch Roman Limes Zone. LAC 2014 Proceedings, 0(0), 13. https://doi.org/10.5463/lac.2014.62