Tag Archives: simulation

Simulating the Past to Understand Human History, 3-5 September 2014, Barcelona

As this year’s conference season is drawing to its end, I thought I’ll share a few reflections on the very successful SPUHH (Simulating the Past to Understand Human History) meeting in Barcelona. A satellite  event to the SSC (European Social Simulation Conference), it was one of the largest gatherings of archaeological modellers I’ve ever witnessed. The organisers filled three days with back-to-back presentations of archaeologically inspired simulation models intertwined with discussions, a great keynote by Tim Kohler and loads of social events (that 9 courses conference dinner will most decidedly go down in history!), and all that against the fantastic backdrop of Barcelona. In a word, it was a blast.

It was also a good round-up of all the, currently, ‘hot’ topics in archaeological simulations. So for all of those who missed it, here’s a short summary divided into the general themes linking the presented case studies. The breakdown may look familiar to some of you as many of the topics repeat from one conference to another. Follow the links if you want to learn more about the case studies presented and you can find the full conference schedule here.

  • Dispersals, demic and cultural diffusions

In the classic ‘was the spread demic (people) or cultural (ideas)?’ ABM-genetic model the team from the Okayama University, Japan  led by N. Matsumoto and M. Sasakura shared their results on the Jomon-Yayoi transition, while in a similar vein but using classic diffusion equations J. Fort’s team presented their newest take on the Neolithic spread. The latter topic is easily the most popular case study among modellers and a number of other presentations focused on that subject. Pérez-Losada showed a detailed sensitivity analysis highlighting the effects of of different parameter values on the diffusion, while Crema and colleagues used it as testing grounds for evaluating the advantages of the ABC (Approximate Bayesian Computation) for determining the relative probability of tested scenarios.  Timm and colleagues gave a presentation about  a recently launched project focusing on Pleistocene dispersal. They mostly discussed the challenges the are facing in creating such a complex, multi-scale model given the dearth of available data but it’s definitely worth watching this space as the project unravels. This topic was also pursued by myself but with a special focus on the demographic dynamics of the dispersal. Finally F. Del Castillo and J. A. Barceló’s were on the other end of the demic to cultural diffusion spectrum with their model of cultural standardisation among hunter-gatherers and agrarian societies.

  • Land use and landscape mobility coupled with resource acquisition/foraging models

In this category, T. Baum gave a fantastic example of tackling simple research questions while exploring the underlying complexity of the system. His land-use simulation was build to figure out why the famous pile-dwelling settlements around Lake Constance (Germany) were so often moved from one place to another. A number of other case studies (Janssen and Hill, Oestmo et al., Lancelotti et al. and Saqalli et al., the last one being particularly worth mentioning ) were similarly focused on the land use and resource distribution over the landscape. With a more methodological focus O’Brien compared potential trackways through marshy area generated by a GIS-software with those simulated in NetLogo. Finally, Olševičová and A. Danielisová  integrated land use models to drive their simulation of the rise and collapse of a Celtic oppidum (you can check out the details of their impressive model combining cellular automata, agent-based modelling and system dynamics here).

  • Case studies of historical events

It is easy to notice that the younger the more detailed and less abstract the models become. T. Brughmans and J. Poblome roman trade model and Fulminante and colleagues’ urban dynamics model were presented as simple networks of interaction but younger case study, such as J. Riley Snyder and O. Dilaver’s model of gigantic aqueduct connecting Constantinople with mainland Greece, P. Murgatroyd and V. Gaffney’s simulation of the march of the Byzantine army or K. Comer and K. Comer model of emergent commercial partnerships in renaissance Italy, were developed with a lot of details.

There were also a few models more general and therefore more difficult to classify such as G. Bogle’s combination of agent-based modelling and canonical theory, H. Inoue and C. Chase-Dunn’s model investigating the evolution of global inequality or N. Gotts  discussion on the role of communication technology throughout the ages.

In the introduction to the ‘Simulating Change. Archaeology into the 21st century’ Andre Costopoulos and Mark Lake complained about the weakness of the archaeological simulation and the scarcity of computational modelling practitioners. It looks like since their 2004 session at the SAAs a lot has changed, particularly in numbers but also in the breadth of the applications and techniques. The final outcome of the SPUHH conference was a long discussion on the need for an better integration of the field and more communication  with the general archaeological audience in order to bring simulation into the archaeological mainstream. So watch this space, there’s some great stuff coming up soon!




Review: Simulating Social and Economic Specialization in Small-Scale Agricultural Societies

Photo of adze head, Mesa Verde National Park. Author’s hands in picture for scale.

Humans are really good at doing multiple different things. If you look at Homo sapiens we have a vast amount of different types of jobs—we hunt, we gather, we farm, we raise animals, we make objects, we learn. Some individuals might be good at one job, and some individuals might be better at another. This is okay, though, because by specializing in what each individual does well we can have a well-rounded society.

But where do we get a switch from generalist to specialist behavior? In small-scale societies, where is the switch from every household making ceramics, to one household making ceramics for the whole village? Specialization only works when there is enough exchange among the individual nodes of the group, so that each specialist can provide their products to the others.

Cockburn et al. in a recent paper for the Journal of Artificial Societies and Social Simulation (JASSS) explore the effects of specialization via agent-based modeling. While the degree to which agents specialize is in some instances unrealistic (Ancestral Puebloans were not able to store 10-years of grain—it would have rotted; also nobody probably specialized in gathering water), Cockburn et al. are aware of this, and state that by using “unrealistic assumptions, we hope to, as Epstein (2008: 3-4) says, “illuminate core dynamics” of the systems of barter and exchange and capture “behaviors of overarching interest” within the American Southwest.”

So, what are these behaviors of overarching interest? Well, for one, specialization and barter lead to increasing returns to scale, allowing for denser and larger groups as well as higher populations than when individuals do not specialize. Also, the networks that formed in this analysis were highly compartmentalized, suggesting that certain individuals were key to the flow of goods, and thus the survival of many people. Cockburn et al. suggest that the heterogeneity of the networks may have helped individuals be more robust to critical transitions, as Scheffer et al. (2012) suggest that modular and heterogeneous systems are more resilient.

This paper should be of interest to our readers, as it combines both agent-based modeling and network analysis, trying to shed light on how Ancestral Puebloans lived. One key drawback to this article is its lack of comparison (in goodness-of-fit measures) to the archaeological record, leaving the reader wondering how well the systems described would fit with archaeological output. Kohler and Varien, in their book on some of the early Village Ecodynamics Project work, develop various goodness-of-fit measures to test the model against archaeology. Perhaps Cockburn et al. intend to use their work with some of these goodness-of-fit measures in the future.

However, despite this drawback, the article does help illustrate highly debated questions of specialization vs. generalization in the archaeological record. Could people have specialized? Yes. Does specialization confer a benefit to individuals? Yes. Taking this article in tandem with debates on specialization may help us to come to a consensus on how specialized people were in the past.

Please read the open access article here:



–Stefani Crabtree

Why Simulating Complexity?

The Tale of Complexity Science

In the old science things were simpler. As a scientist you would look at a problem, dissect it into components and then research them one by one. The general belief held that the more you know about the components the better you understand the system and hence the closest you are to solving the problem. And so we run in a similar vein for a few hundred years and we were doing just fine.
Until a few decades ago, when more and more voices started uttering uncomfortable questions and pointing out to some perplexing results of their research. Normally, you could dismiss pretty much any piece of research which gives counterintuitive results but this time the fussers were using two things that are really difficult to dispute: maths and computers. And so, as Thomas Kuhn would call it, a paradigm shift started to happening.

The Theory of Complexity

The definition of complexity is multidimensional and often varies adjusting itself to the interests and assumptions of a given scientific field. There are, however, a few core elements that seem to characterise complex systems regardless if they’re made out of particles, neurone, mice or men. You can start with what it says on the tin: complexity science deals with complex systems. And a complex system is a system consisting of a large number of independent components which interact with each other and sometimes also with the environment, a good example of which is a society, an ant hill or a brain. So far, this is in line with what we called the ‘old science’ view of the world but there is a key difference. In the complexity science view the interactions between the system’s elements, even if following very simple rules, can lead to large-scale complex patterns which would be difficult to predict just by looking at the individual components.

A great example of which is a flock of birds – it is a magnificent show involving hundreds of birds creating one multi-element “organism” able to effectively escape predators, travel thousands of miles but also, on a cheerful note, turn the Italian capital into a smelly pile of bird’s poo. It may come as a surprise, but bird flocks are leaderless and it was shown that the individual birds need to follow only three rules to form a flock: 1. align with nearby birds; 2. adhere to the nearby birds; 3. but try to avoid collisions (don’t believe? check out the simulation for yourself: NetLogo Flocking).

Other commonly quoted examples of complex systems include: human brain, global economy, cities, ecosystems etc. They often have a great ability to adapt rapidly to the constantly changing circumstances but they’re also prone to very dramatic changes if a specific threshold is crossed (think economic crashes). All this happens without a centralised control, a leader and often without any influence of exogenous factors (you may be mortified to learn that trafic jams can occur with no reasons whatsoever).

Complexity Science and Archaeology

The main two tools of complexity science are simulations and network analysis. A number of successful applications of both of those techniques to archaeological case studies showed the great potential of this framework. This includes a great case study of growing the artificial society of the Anasazi, getting to better understand early hominid’s behaviour and food sharing or the numerous simulations of the Neolithic wave of advance. However, at the moment complexity science in archaeology is undergoing its adolescence growth spurt with the number of new applications growing exponentially. Being still in its formative phase it is vulnerable to uncritical applications and it’s lacking a solid theoretical framework although its potential is being more and more widely recognized.

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Network created by agents moving between specific points in the landscape


Complexity science offers an exciting opportunity to bring archaeology closer to more quantitative approaches as the tools it uses enforce binary description of the system. It is also a fantastic playground for testing out new (and old) models. Luke Premo called the simulation environment a ‘behavioural laboratories’ in which one can “eliminate plausible scenarios that are nevertheless unlikely to have occurred” ( Premo 2006, 108). Finally, complexity science tools are the only way in which one can fully understand the nature of complex systems of which past human societies are definitely one.