Category Archives: Noteworthy Publications

French Wine: Solving Complex Problems with Simple Models

What approach do you use if you have only partial information but you want to learn  more about a subject? In a recent article, I confronted this very problem. Despite knowing quite a bit about Gaulish settlements and distributions of artifacts, we still know relatively little about the beginnings of the wine industry. We know it was a drink for the elite. We know that Etruscans showed up with wine, and later Greeks showed up with wine. But we don’t know why Etruscan wine all but disappears rapidly within a few years. Is this simple economics (Greek wine being cheaper)? Is this simply that Etruscan wine tasted worse? It’s a question and a conundrum; it simply doesn’t make sense that everyone in the region would swap from one wine type to another. Also, the ceramic vessels that were used to carry the wine—amphorae—those are what we find. They should last for a while, but they disappear. Greek wine takes over, Greek amphorae take over, and Etruscan wine and amphorae disappear.

This is a perfect question for agent based modeling. My approach uses a very simple model of preference, coupled with some simple economics, to look at how Gauls could be drivers of the economy. Through parameter testing I show that a complete transition between two types of wine could occur even when less than 100% of the consumers ‘prefer’ one type.

Most importantly in this model, the pattern oriented approach shows how agent-based modeling can be useful for examining a mystery, even when the amount of information available might be small.

Check the article out on the open source MDPI website.

Everything you ever wanted to know about building a simulation, but without the jargon

I think everyone who had anything to do with modelling came across an innocent colleague/supervisor/another academic enthusiastically exclaiming:

“Well, isn’t this a great topic for a simulation? Why don’t we put it together – you do the coding and I’ll take care of the rest. It will be done and dusted in two weeks!”

“Sure! I routinely build well-informed and properly tested simulations in less than two weeks.” – answered no one, ever.

Building a simulation can be a long and frustrating process with unwelcome surprises popping out at every corner. Recently I summarised the 9 phases of developing a model and the most common pitfalls in an paper published in Human Biology: ‘So You Think You Can Model? A Guide to Building and Evaluating Archaeological Simulation Models of Dispersals‘. It is an entirely jargon free overview of the simulation pipeline, predominantly aimed at anyone who want to start building their own archaeological simulation but does not know what does the process entail. It will be equally useful to non-modellers, who want to learn more about the technique before they start trusting the results we throw at them. And, I hope, it may inspire more realistic time management for simulation projects 🙂

You can access the preprint of it here. It is not as nicely typeset as the published version but, hey!, it is open access.


Baby Boom and the Old Bailey: Two New Data Mining Studies

Photo from

Here at simulating complexity most of us know Tim Kohler for his pioneering work on the Village Ecodynamics Project, one of the first major agent-based modeling projects in archaeology. In a new study in PNAS, “Long and spatially variable Neolithic Demographic Transition in the North American Southwest,” Kohler and Reese shift from simulation to real archaeological data analysis.

This article has been highly cited in the news (here, here, and here to name a few) mostly due to its enticing moral: there was a huge baby boom in the Southwest, it was unsustainable, and thus there was a mortality crash. This can be extrapolated to where we are today. If the Southwest couldn’t handle that many people, how many can our fragile Earth handle?

But the most applicable part of their study for this blog is the data mining aspect of the article. Reese literally spent 2+ years pouring over the grey literature to compile data on skeletons from the area, classifying their ages, sex, and various other data. Then these data were entered into a giant spreadsheet, where they were subject to the analyses that yielded the results.

Many archaeological projects are looking at large datasets and trying to find patterns in the noise. This paper is just one of many that is making use of the vast amounts of data out there and finding ways to synthesize massive reports. Gathering this data requires hours of work that is often times by hand.

In another study in PNAS “The civilizing process in London’s Old Bailey,” (also written about in the media here and here among others) Klingenstein, Hitchcock and DeDeo analyzed 150 years of legal documents from the Old Bailey in England. They find that through time there is a differentiation between violent and nonviolent crime, which reflects changes in societal perception of crime. With so many documents, standard methods of pouring through gray literature by hand would have been impossible. Instead, they invent techniques for a computer to read the documents and classify different words to different types of crimes. This study is not an archaeological study, but shows how historical documents can be used to find patterns in a noisy system.

Both of these studies demonstrate how our way of thinking of data is changing. Archaeologists used to focus on one site or one time period. These two studies demonstrate how creative thinking, quantitative knowledge, and some approaches from complexity science can help us find patterns in gigantic datasets. I recommend reading both studies, as they may help inspire you to think about some of your big data sets, and how you can approach them.

Review: Trends in Archaeological Simulation

For a subject with a comparatively short history, the history of computational modeling in archaeology has been written many times before. The earliest attempt to establish a chronology of archaeological simulation appeared in Doran & Hodson’s Mathematics and Computers in Archaeology. This was followed over the next decade and a half by reviews by Dyke in 1982, Bell in 1987, and Aldenderfer in 1991, all of which were more or less pessimistic about the sum of contributions from archaeological simulation (with Chippindale portending its untimely demise at the hands of paradigm-fickle prehistorians with a quantitative bone to pick).

In the recent special issue of the Journal of Archaeological Method and Theory, Lake reverses course on what might be described as a rather grim assessment of simulation’s prospects presented in a special session at the 2005 SAA meeting. In that paper (published in the Simulating Change edited volume from Utah Press), he argued that archaeological simulations suffered from overdevelopment and limited application. As a result, simulation studies were likely to remain marginalized because much of the archaeological methods and theories of the day were ill-suited to make appropriate use of them.

In this new paper, titled “Trends in Archaeological Simulation”, Lake credits a revival in archaeological simulation to the advent of agent-based approaches and to simulation finding its niche in areas such as human evolution, dispersal, and household decision-making. Like other reviews (such as those appearing in Simulating Change), it breaks down the history into distinct phases. But rather than apply the first/second/third wave scheme, Lake uses a more refined timeline in which periods may overlap one another to some extent as a result of publication lag:

  • An early Pioneer phase, taking place between the late 1960s and early 1980s, prompted in no small part by Doran’s 1970 exhortation , and featuring the works of Thomas, Zubrow, and Wobst.
  • A Hiatus phase, almost entirely ensconced in the 1980s, when archaeological simulation was reeling from the post-processual critique and the recognition of computational limitations.
  • A Renaissance phase, taking place mostly within the 1990s and continuing into the early 2000s, heralded in part by the publication of edited volumes by Mithen, Kohler and Gummerman, and McGlade and van der Leeuw.
  • An Expansion phase, from the beginning of the 21st century, in which archaeological simulators begin to control their own destiny

What sets this review apart from others is Lake’s contextualization of the trends, particularly those during the 1990s. For example, the 1980s and 1990s are frequently considered to be a period when archaeological simulation was in decline, coinciding with the postmodern critique and initial disappointment in technical constraints imposed by mainframe computing of the day, and evidenced by drooping publications on the subject. Lake agrees that the 1980s represented a hiatus (although this downplays the important contributions made during that time, particularly Reynolds’ work on the Guila Naquitz project), but he argues that while the number of papers applying simulation did not increase appreciably during 1980s or 1990s, projects became longer-lived, showed increased utility, and the focus shifted away from highly specialized simulations on the periphery of larger traditional studies to more generalized applications centered on simulation. This trend is, in part, due to interest from both archaeologists and simulators in complex systems theory, a shared interest which has had an annealing effect on the theoretical position of simulation within the discipline. Rather than simulation being a niche tool used for dramatic effect by those with computer programming skills, simulation is viewed by some as an essential way of getting down to the tasks of archaeological heuristics and explanation.

There are several areas that Lake argues have seen growth for archaeological simulation: reaction-diffusion models, long-term societal change or human-environment interactions, and human evolution. Many of these have benefitted from the advent of agent-based modelling, and its successful wedding to GIS. Some are large, multi-component models, while others remain abstract, incorporating as few variables as possible. Premo’s response to Barton et al.’s paper of Neanderthal settlement patterns is a good example of this latter type, as well as the recent paper by Vegvari and Foley on cultural complexity reviewed here last month. Lake argues that these types of papers are accounting for a larger share of archaeological simulations, and that rather than being superfluous to a larger study, these often are simulation-centered studies.

There is a final set of simulations Lake calls “miscellaneous”, and these include Surovell and Brantingham’s important use of simulation to understand taphonomic biases in the use of cumulative radiocarbon data, and a recent study by Rubio-Campillo et al. which melds ABM and GIS to simulate historic battles with the aim of understanding the distribution of musketballs over space. Lake argues that what binds these models together is an interest in testing archaeological methods; this is true, there’s more to them than simple method-checking. In many of the studies that have been conducted, particularly over the past decade, the target to be generated is a social or demographic phenomenon, which itself has typically been constructed from traditional archaeological inference.  The target of these “miscellaneous” studies, on the other hand, is the number, arrangement, and qualities of objects in the archaeological record itself. If archaeological simulation continues to grow and become more incorporated into the mainstream as predicted, it will be interesting to see how the theoretical connection between model outputs and the stuff in the ground plays into justifications for and critiques of future simulation in archaeology.

After re-reading Lake’s opinion of archaeological simulation in 2005, one can’t help but agree that its pessimism was hasty (perhaps even untimely with its publication in 2010), and that “a real increase in the use of simulation was underway” even before it was being written. This new paper could serve as a touchstone for that movement, one only achievable now that some of the fledgling projects of the 1980s and 1990s have come to full fruition and encouraged a new generation of simulators. It offers a clear narrative of how simulation in archaeology has changed over time, a sense of how it came to produce the diverse types of studies now being published, and perspective on where it may be headed.

Featured image: Old Timey Computer With Black Keys by user realityhandbook

The tragedy of the commons

Imagine a field at a north-eastern fringe of your village. Everyone’s allowed to use it as a pasture so you always see a lot of cows there. Some belong to you, some to your neighbours. As an intelligent homo economicus you know that if too many cows pasture on the same strip of land the grass gets depleted quicker than it can regrow and the land becomes useless. Nevertheless, you also know that any benefit of using the pasture for your cows means benefit for you, whereas any damage caused to the pasture is shared equally among all neighbours. The logical conclusion: take as much advantage of the shared land as possible. The tragedy of the commons arrises from the fact that all of your neighbours concluded the same and soon all the grass is gone.

Although this seems like a topical story in the world of climate change, dwindling resources and horror stories of imminent doom, it is easy to notice that, generally, human societies have developed many successful strategies to deal with this problem. You can call them ‘institutions’, be it a socially observed rule, superstition or an actual person whose job it is to catch and punish free riders.

In their new paper “The co-evolution of social institutions, demography and large-scale human cooperation” Powers and Lehmann look at the evolution of such social institutions and ask the question: is social organisation inevitable?

I wanted to share it here as this is a fantastic example of how much you can achieve by formalising a system and running a relatively simple simulation. In just a few equations Powers and Lehmann put together the relationship between populations of social and asocial individuals, the competition and cooperation between them, the interplay between the available resources and the population growth as well as the process of sanctioning free riders. On top of that they made the simulation spatial which turned to be a key factor for understanding the dynamics of the system.

It turns out that in a well mixed population neither the socials nor the asocials can take over forever (i.e. maintain the stable equilibrium). However, if the groups live on a spatial grid (just like us – humans) the situation looks different. The population of social agents cooperate to create strategies, which pushes up the ceiling of the carrying capacity for the group.  This means that the group can grow and expand into the neighbouring areas and once they arrive they are to stay. The fact that their carrying capacity ceiling is higher than that of the asocial individuals means that the population remains stable ad infinitum. Interestingly, the amount of resource spent on sanctioning the potential free riders usually fixate on pretty low numbers (10-20%). Therefore, this simulation shows that cooperation between agents coupled with even a small investment into ‘institutions’ leads to dramatic changes in the structure of the group.  A population of cooperative agents is likely to take over asocial neighbours and turn into a hierarchical society.

Although the model is largely abstract, its findings are particularly applicable, as the authors note, to the shift between hunter-gatherer groups and sedentary agriculturalists. A strong cooperation among members of the latter group is necessary for constructing irrigation systems. These, in turn, increase the group’s carrying capacity leading to a higher population size, at which point, sanctioning of the potential free riders becomes a necessity. And so Ms Administration is born…

On the final note, it’s worth taking a good look at Powers and Lehmann’s paper if you’ve never come across EBM (Equation-based Modelling). First of all, this is a fantastic example of how to formalise a complex system. The equation terms represent simplified reality. A good example of this is the group cooperation. The model assumes that people cooperate to make their work more efficient (i.e. to lift their carrying capacity), it doesn’t go into details of what that means in any particular case – digging a canal together or developing better shovels. It really doesn’t matter. Secondly, the authors did a particularly good job in explaining their model clearly, you really don’t need anything beyond primary school maths to understand the basics of the simulation.

We (archaeologists) have been discussing the rise of complex states with their administration and hierarchy for decades (in not centuries) and the question “why?” is always in the very core of all research: why did people get together in the first place? why did they cooperate? why did the hierarchy emerge? Powers and Lehmann’s model takes us one step closer towards answering some of those questions showing how simple interactions may lead to very complex outcomes.

Urban Scaling, Superlinearity of Knowledge, and New Growth Economics

View of Mumbai, from

How far did they fly? …not very far at all, because they rose from one great city, fell to another. The distance between cities is always small; a villager, traveling a hundred miles to town, traverses emptier, darker, more terrifying space.” (Salman Rushdie The Satanic Verses p. 41)

Compelling recent work from folks at the Santa Fe Institute suggests that both modern and ancient cities follow similar growth patterns. As cities grow, and if they are regular in layout, it becomes easier to add roads, add parks, add public buildings. You no longer need to invest large amounts to build the infrastructure. It’s easier to add length on to existing roads than it is to create a new road altogether. This phenomenon is knows as increasing economies of scale. Bettencourt found that in modern cities, infrastructure and public spaces both scale to the population at an exponent of between 2/3 and 5/6. Ortman et al. found that the same exponent works to explain population growth and infrastructure in the prehispanic Valley of Mexico.

Okay, what does this mean? Ortman suggests that principals of human habitation are highly general, and that there may be an inherent process to settlement. What’s remarkable in this study is how parallel the growth processes are between ancient and modern cities. Would a modern Saladin Chamcha feel as at home not only in modern Mumbai and London, but also in medieval London or classic Teotihuacan? Is the distance between cities truly small, as Rushdie (via Chamcha’s character) suggests?

Maybe so. Cities, both teams argue, are social reactors. Cities amplify social interaction opportunities. We may expect that things like the number of patents awarded for new inventions would scale linearly with growth, but this isn’t so. It turns out that the number of patents scales superlinearly as do other measures of modern output. With more density comes more creativity.

Infrastructure scales sublinearly, and output scales superlinearly. The larger the city, the less has to be spent to create more infrastructure. The larger the city, the more we can expect to have more intellectual output, like increasing quantities patents.

And, to say it again, this is not true just of modern cities, but prehistoric ones as well.

This brings us to the question of GDP and new growth economics. It turns out that just measuring labor and output does not calculate GDP, but there is an additional, unknown factor, which economists call the A factor. That factor is knowledge. This superlinearity of output in cities, of things like invention and patents, is this that extra A-factor and do we see it rise superlinearly due to the density of networks in cities? And can we truly see prehistory and moderninty working in similar ways? It turns out it’s really difficult to measure the A-factor (economists have been trying for a while), but maybe we’re seeing the effects here.

Ortman et al. argue:

“all human settlements function in essentially the same way by manifesting strongly-interacting social networks in space, and that relative economies and returns to scale (elasticities in the language of economics) emerge from interactions among individuals within settlements as opposed to specific technological, political or economic factors” (Ortman et al. 2014, p. 7).

While Saladin Chamcha might not have been able to communicate with inhabitants in Teotihuacan, he would have felt at home. The city would have held similar structures to 1980s London—he could find a center, a market, a worship space, and those things would have scaled to the size of the population. As humans we build things in similar ways. Bettencourt and Ortman’s work is compelling and causes us to think about how our brains function, how we establish social networks, and what common processes there might be across humanity, both spatially and temporally.

To read Ortman et al.’s work, see this link in PLoS ONE

To see Bettencourt’s work, see this link in Science

A new recipe for cultural complexity

In their new paper Carolin Vegvari and Robert A. Foley (both at University of Cambridge) look at necessary ingredients for the rise of cultural complexity and innovation in their recent paper in PloS One.

The question of cultural complexity is an anthropological mine field.

Neolithic diversity of tools. Sourceéolithique_0001.jpg
Neolithic diversity of tools. Sourceéolithique_0001.jpg

To start with,  the definition of ‘cultural complexity’ is controversial and difficult to quantify even if we concentrate solely on material culture. Should we count the number of tools people use? But that would be unfair towards more mobile societies who, understandably, don’t like carrying tons of gear. So maybe we should look at how complex the tools themselves are? After all, a smartphone contains more elements, combined in a very intricate way and performs more functions than, say, a hammer. It doesn’t work well in a nail-and-wall situation though. In fact, the differences in the amount and complexity of material culture among contemporary hunter-gatherers is: “one of the most dramatic dimensions of variation among foragers (…). Some foragers manage to survive quite well with a limited set of simple tools, whereas others, such as the Inuit or sedentary foragers, need a variety of often complex tools.” (Kelly 2013, 135).

The rise of cultural complexity and especially the factors that contribute to it and the conditions that need to be met are therefore a big unknown in anthropology and  archaeology alike. Similarly to all scientists we like big unknowns so a number of models have been developed to investigate various recipes for cultural complexity quite often involving radically different ingredients.

Since early 2000s (I suspect Shennan 2001 was the seed for this trend) one of the favourite ingredients in the cultural complexity mix was the demography and the population size in particular. In very simple terms, the hypothesis goes that only large groups, which can sustain a pool of experts from whom one can learn a given skill, will exhibit higher cultural complexity.

The Movius Line

And this was actually applied to archaeological case studies, for example by Lycett and Norton (2010). They argued that the notorious Movius Line slashing through the Lower Palaeolithic world is a reflection of lower population density in the south-east, central and north Asia causing the groups to drop the fancy Acheulean handaxes and to revert to the simpler Oldowan core-and-flake technology.

Vegvari and Foley’s paper is a new stab at the issue. Their simple yet elegant Agent-based model consists of a grid world on which agent groups forage on depletable resource according to their skill level represented as a list of generic cultural traits. These traits can be improved to achieve higher efficiency in extracting the resources and new traits can be invented.  Vegvari and Foley tested a number of scenarios in which they varied group size, selection pressure (really interestingly constructed as a factor lowering the efficiency of resource extraction from the environment), different costs of learning and the ability to interact with the neighbouring groups.

The results of the simulation are really interesting. Vegvari and Foley identified the good old natural selection and its friend population pressure as the main drivers behind the increase in cultural complexity. Although, they work hand in hand with the demographic factors, the population size is a bit of a covariant. Lower population size means less competition over the resource, i.e. lower population pressure. It will, therefore, correlate with the cultural complexity but mostly because it is linked to the selection pressure.

Interestingly, the learning cost came as another important stimulant for groups under high selection pressure and those who could interact with their neighbours as it increase the population pressure even further. Finally, Vegvari and Foley recognised a familiar pattern of the sequential phases of logistic growth.

The logistic curve of population growth.
The logistic curve of population growth.

It starts with the population climbing towards their relative carrying capacity (= the maximum of resource they can extract from a given environment), when they reach the plateau they undergo a strong selection pressure, which leads to innovation. A new cultural trait allows them to bump up the carrying capacity ceiling and so the population  explodes into the logistic growth and the cycle repeats.

Vegvari and Foley created a simple yet very robust model which tackles all of the usual suspects – demographic factors, natural selection and the cost of cultural transmission. It shows that the internal fluctuations of a population arising from simple social processes can induce complex population dynamics without any need for external factors such as environmental fluctuations.  And this is a fantastic opening for a long and fruitful discussion in our discipline.


Kelly, Robert L. 2013. The Lifeways of Hunter-Gatherers. The Foraging Spectrum. 2nd editio. Cambridge: Cambridge University Press.

Lycett, Stephen J., and Christopher J. Norton. 2010. “A Demographic Model for Palaeolithic Technological Evolution : The Case of East Asia and the Movius Line.” Quaternary International 211 (1-2): 55–65. doi:10.1016/j.quaint.2008.12.001.

Shennan, Stephen. 2001. “Demography and Cultural Innovation: A Model and Its Implications for the Emergence of Modern Human Culture.” Cambridge Archaeological Journal 11 (1): 5–16. doi:10.1017/S0959774301000014.

Vegvari, Carolin, and Robert A. Foley. 2014. “High Selection Pressure Promotes Increase in Cumulative Adaptive Culture.” PloS One 9 (1): e86406. doi:10.1371/journal.pone.0086406.