SCCS2014 CfP is now open

We have already mentioned the Student Conference on Complexity Science which will be held in Brighton between 19th and 22nd August. They have just released their Call for Papers. This is a fantastic opportunity to check out how other disciplines are playing with complexity science and, a little bird told me, that apart from a strong representation in social sciences and artificial societies they may be a whole archaeology & history session!

For more info have a look at their Call for Papers below.

We are please to announce that the Student Conference on Complexity Science (19-22 August 2014, Brighton, UK) Call for Papers is now open. The deadline for submissions is 14th April at 12 p.m. (UCT).

The Student Conference on Complexity Science (SCCS) is the largest UK conference for early-career researchers working within the interdisciplinary framework of Complex Systems, with a particular focus on computational modelling, simulation and networks analysis. Confirmed keynote speakers include prof. Mark Newman (University of Michigan, USA), prof. Eörs Szathmáry (Eötvös Loránd University, Hungary) and prof. Nigel Gilbert (University of Surrey, UK). This year SCCS will consist of five hands-on workshops as well as parallel sessions. The topics will oscillate around ten general themes listed below. Please note that abstracts not falling directly into one of the general themes may still be considered if they are relevant to a specific session.

Theory of Complexity Science

Self-organization, nonlinear dynamics and chaos, mathematical and simulation methodology

Network Science

Technological networks, spatial networks, infrastructure, ecology, social networks, Internet

Planning and Industry

Critical infrastructures, urban planning, mobility, transport, sustainability

Earth System Complexity

Climate change, ocean, atmosphere, ice and solid earth dynamics

Biological Complexity

Systems biology, ecology, ecosystem services, medicine

Evolution and the Origin of Life

Evolutionary systems, origin of life theory, major evolutionary transitions, generative and developmental systems, artificial life

Artificial Intelligence

Swarm intelligence, embodied cognition, robotics, neuroscience

Social Systems

Linguistics, demography, psychology, health, past societies

Economics and Finance

Markets and stability, trade, public policy, game theory

Engineering and Physical Sciences

Quantum dynamics, statistical mechanics, optimisation, turbulence, computational chemistry, nanotechnology, energy

If your work comes under the umbrella of complexity science, then we want to hear from you! To submit your abstract follow this link:

For more information visit our website, follow us on Twitter @SCCS2014 or Facebook


Stefani’s current project has been featured in the Scientific American blog! If you want even more details check out her blogpost about it, here.

Is the universe a simulation?

A recent NY Times op-ed reintroduces the philosophical concept of the simulation hypothesis: the idea that the universe we live in is an elaborate computer simulation.

This is kind of based on the idea that mathematics has rules that, while expressed in a human-derived conceptual language, exist in a plane unto themselves. This concept has been explored by folks like Eugene Wigner, but the simulation hypothesis is still certifiably fringe from what I can tell. It would hold that these rules are what controls our simulated existence, and that each time we learn something about them, we’re pulling back the curtain just a little more.

The op-ed featured some recent mathematical research into this topic, which is looking for “observable consequences” of being in a simulation (the fringy-ness should be apparent in the opening to the conclusion: “In this work, we have taken seriously the possibility..”). These folks are saying “if the simulation were a simulation like this (in this case, a latticed hypercube of time-space), we should be able to detect how that world was set up using physical assessments of certain known phenomena (in this case, high energy cosmic rays). They conclude that as long as there is some limit to the resources available to the simulators, there must be ways of detecting the spacing within the lattice. Other research has focused on detecting this through changes in gravity around black holes in universes of different dimensions. It is interesting to me that the solutions proposed by these researchers, at least as described here, follow a method similar to that of Grimm et al.’s pattern-oriented modeling.

Working in simulation brings up plenty of epistemological issues regarding scientific representation. I think some of the most important of these for archaeologists are those which deal with relationship between a modelled entity and its real counterpart, and the nature and validity of computational “experiments”. Of course, that all becomes more or less moot if we are only part of a simulation ourselves. But what has me puzzled on this existential level concerns the more general role of simulation. Simulations are usually models, or ways of representing the interacting variables within a more complex system. They’ve been described elsewhere as “tools to think with”, a feature of the upcoming workshop at the CAA. But if our universe is a simulation, in the sense of a model, then what more complex phenomenon is our universe a model of?

Photo credit:  Sergey Galyonkin via Wikimedia Commons

This Year we all Go to Barcelona

In the tough world of Academia there is nothing better than a conference in a city boasting with vibrant research community and a beach. For archaeologists working with computational modelling Barcelona fits the bill nicely and this year no excuses are needed to visit this absolutely fantastic city. After the success of the ECCS2013 last September Barcelona seems to become the world capital for modelling social complexity with at least three major conferences scheduled for 2014.

European Social Simulation Association Meeting

The European Social Simulation Association  will hold its annual meeting  at the Universitat Autònoma de Barcelona between 1-5 September, 2014.  You can find more information about the conference here.

But more importantly, our colleagues from the SimulPast project are organising a satellite event Simulating the past to understand human history. Although the conference is aimed at showcasing the achievements of the SimulPast project, the wide range of topics indicated by the organisers shows that we can expect a good set of interesting papers dealing with different aspects of modelling and complexity science applications in archaeology.

Finally, the conference is worth a trip even if only for the keynote speakers.  Timothy A. Kohler (Washington State University) is known from his Village Ecodynamics Project and, probably by all students thanks to his classic paper “Complex Systems and Archaeology” in Ian Hodder’s Archaeological Theory Today.  And we can only hope that someone will ask  Joshua M. Epstein (Johns Hopkins University) – the second keynote speaker  – his trademark question ‘Why model?

The Call for Papers closes this Thursday ( 28th, February, 2014).  According to the CfP the organisers look for a wide range of papers diverse in both archaeological but also methodological scope: Applications are welcomed on all subjects (from Anthropology, Archaeology, Geography and History) using different approaches to social simulation and presenting case studies from any region of the world and any prehistoric or historic period. Theoretical aspects of social and cultural evolution are also encouraged.

Coincidentally, at the same time (1-7 September) Burgos will be hosting the XVII Congress of the International Union of the Prehistoric and Protohistoric Sciences (UISPP). You can find the list of session here.

European Conference on Social Networks

The second complexity science conference held this year in Barcelona is the 1st European Conference on Social Networks between 1-4, July, 2014. They haven’t opened their CfP yet so only preliminary information are available on their website but it looks as if it was going to have a strong archaeological twist.

SocInfo 2014

Finally, the 6th International Conference on Social Informatics, although focused more on present rather than past human societies, may also be of interest to many, especially in light of the conference mission statement: This year’s special purpose of the conference is to to bridge the gap between the social sciences and computer science. We see the challenges of this as at least twofold. (..)  emphasis on the methodology needed in the field of computational social science to reach long-term research objectives. We envision SocInfo as a venue that attracts open minded researchers who relax the methodological boundaries between informatics and social sciences so to identify common tools, research questions, and goals. SocInfo will be  held in Barcelona between 10-13 November 2014.

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

Connected Past in Paris

For all you network analysis fanatics out there, a quick reminder that the Connected Past Conference is happening for the second time this April. Since the  beginnings as a TAG (Theoretical Archaeology Group Conference) session in 2011 in Birmingham the Connected Past team has been bringing together researchers working on network analysis and successfully promoting this core complexity science technique among archaeologists.

Looking at the conference programme there is a good mix of applications typical to archaeology such as modelling ancient trade  (Eivind Heldaas SelandFrancisco Apellaniz) or interpreting the distribution of archaeological finds (Henrik Gerding and Per ÖstbornHabiba, Jan C. Athenstädt and Ulrik Brandes), but also quite a lot of historical case studies ranging from ancient writers (Thibault Clérice and Anthony Glaise) to early modern financial networks (Ana Sofia Ribeiro) to modern academic networks (Marion Beetschen).

Thanks to a leak from one of the organisers  we know that there are literally only a few places left so book asap to avoid disappointment:

Flocking: watching complexity in a murmuration of starlings

My father is a bird watcher. One of my earliest memories is watching a giant flock of wild geese in ponds in eastern Oregon. The way the individual birds would react and interact to form what seemed like an organism was breathtaking. I bet my dad didn’t realize that this formative viewing of a flock of waterfowl would influence the way I study science.

This is a video shot by Liberty Smith and Sophie Windsor Clive from islands and rivers that shows, in exquisite beauty, how individual decisions can have cascading effects on the system. By each bird trying to optimize its distance to the bird in front and on the sides, these birds form a flock of birds. Flocking behavior, shoaling behavior in fish, and swarming behavior in insects all have similarities.  Mammals, too, exhibit this behavior when they herd.

Craig Reynolds first simulated this in his “Boids” simulation (1986). The agents (the boids themselves) want to remain aligned with the other agents around them, want to retain separation from the other agents around them, and will steer their heading toward a perceived average of the headings of the other agents around them. These three simple rules produce the complexity of the flock.

Who can forget the iconic scene of the herding wildebeest in the Lion King? My understanding is that this was one of the first uses of computer graphics in an animated film, and the animation followed similar rules to Reynolds’ simulation.

While my father would likely be appalled that I would promote starlings (their negative effects on biodiversity in the Americas is well documented) this video shows flocking behavior perfectly. Enjoy the beauty of complexity.

(And thanks Joshua Garland and Brandon Hildebrand for pointing me toward this video!)

–Stefani Crabtree

Upcoming course: Model Thinking

When my colleagues explained to me what this blog was for, I was really pleased to hear that it would be a forum where a modeling novice could gain some orientation and learn shortcuts that weren’t available, or at least not easy to find, when I was just learning. Modeling, at least the computational side of it, is still in many ways a rarefied specialization in the social sciences, and reliable guideposts are few.

The concept of modeling itself is vast and vague. For some, images of flow-charts and mental maps come to mind. For others, it could mean miniature trains or linear regressions. There are many different ideas about what models are or what they are used for. It doesn’t help that there are two very different career paths that are both called “modeling” (from what I can tell, the crossover rate has been pretty limited).

When someone new begins to dig into the pursuit of modeling, they’re likely to come up against that stumbling block to end all stumbling blocks: MATHEMATICS. Differential equations. Graph theory. Markov chains. Point processes. And coupled to this is an array of seemingly unrelated computer programming languages and development environments with documentation that is not always easy to navigate. If you don’t come from a mathematics or computer science background, it can be difficult to know where to begin. But more importantly, it may not be clear what modeling is actually for or why anyone would ever want to do it.

Enter the Coursera course “Model Thinking”, taught by Michigan’s Scott E. Page. In the first lectures, the reasons why anyone would want to learn about models are laid out in plain English. From the course website:

1. To be an intelligent citizen of the world
2. To be a clearer thinker
3. To understand and use data
4. To better decide, strategize, and design

Page tells us that, in the era of Big Data, the ability to identify key components and apply knowledge are crucial. Data by itself, no matter what quantity, is useless without the ability to harness its informational potential through identifying patterns and understanding processes. Models, it is argued, are just the kinds of tools we need to use data wisely.

It should be stated up front that this is not a course designed to teach you to how to program. There are lots of different courses, tutorials, and other materials on programming which are available, and this site is doing its part to help provide some direction. Instead, Page offers valuable tools for thinking about complex problems using the power of models.

In some ways, it’s like a best-of album: all your favorites are there. Segregation. The Prisoner’s Dilemma. Forest Fires. The Game of Life. These models are used to demonstrate principal concepts in modeling and complex systems, such as aggregation, tipping points, bounded rationality, and path dependence. Real-world case studies are used to show how models like these can illuminate core dynamics in what are otherwise very complex and intractable systems, such as banking networks, electoral politics, or counterterrorism.

The course doesn’t deal outside of mathematics entirely, but introduces the necessary concepts in a fairly straightforward and basic way. The online format suits this well: if you’re not familiar with a concept, you can simply pause the video and Google it.

If you’ve taken a few MOOCs, you know that production counts for a lot, but sometimes it can be distracting. A voice-over with someone’s lecture slides is bound to put you to sleep; too many animations or an overdone background or wardrobe can draw your attention from the lesson. Most of these videos begin with Page in front of a blank background, waist up and gesturing, with key words being displayed at the bottom of the screen. This usually transitions into a demonstration with simple but effective graphics and live-drawn overlays for emphasis (see here). This approach seems to balance the issue of too little/too much production. The weekly quizzes are thoughtful but not overwhelming. I found them particularly good for someone new to MOOCs. In addition, if you’re watching the videos on the Coursera site, many of the lectures will stop part way through and ask a multiple-choice question to make sure you’re paying attention. This does a reasonably good job of reinforcing the lesson.

For someone who does modeling on a regular basis, the course is great for clarifying and compartmentalizing different ideas which you may already be using but don’t know much about their background or how to interface them other other concepts. For someone who is new, it has the potential to shed light on some of the reasons models are used and give some direction in terms of how to use models in your life.

The next session begins on February 3rd and runs for 10 weeks, with a recommended workload of 4 to 8 hours per week. The signup page at Coursera can be found here.

Image “5th Floor Lecture Hall.jpg” courtesy of Xbxg32000 @ Wikimedia Commons

Modelling Across Millennia

Wind back the tape of life to the early days of the Burgess Shale; let it play again from an identical starting point, and the chance becomes vanishingly small that anything like human intelligence would grace the replay.  (Gould 1989; p. 14).

How can we attempt to understand the complexity of life today when we cannot run repeated experiments on the evolution of life? If we could go back to the beginning, would we find that each evolutionary change was contingent upon the previous step? Would stochasticity make every new run of the “tape of life” completely different from the last?

Of course, actually replaying the evolutionary tape to answer these questions is impossible, but through the use of agent-based modeling we may be able to run experiments on a system to understand how the “tape of life” created the complexities we see today.  By exploring the behaviors of agents the modeler can deduce what occurred in real systems. These models allow scientists to study systems in space and time, which are often too large and too long for more traditional measures of study. How to understand Big Data is a problem (see this article for a discussion of Big Data issues) but agent-based modeling provides a way to not only generate that big data, but with proper use, sort it and answer questions of interest that only Big Data can answer.

Repeated calls have recently been made to apply agent-based modeling to contemporary affairs to not only understand crises as they unfold, but also to anticipate them (e.g. see Buchanan 2009; Cabrera 2008; Epstein 2009).  Archaeology is essential for these efforts. It provides that long-term view that a myopic study of our modern problems cannot truly address.

In a 2012 special issue of Ecological Modelling, several archaeologists put forth their uses of modeling to understand past societies, and I would argue, these studies help us further our understanding of current problems. Their geographic regions span from the U.S. Southwest to the South Pacific, the Mediterranean to Mongolia. Crabtree and Kohler provide a good background to the models presented in the issue. They say:

“Modelling of ancient socio-ecological systems is in its infancy. Problems to be faced include both the incompleteness of data imposed by the archaeological record, and the difficulty of developing satisfactory frameworks for characterizing the behavioral plasticity of humans and the evolvability of the cultures they create. We do not pretend that all the problems are yet satisfactorily addressed, but we believe it is important to begin, nevertheless. Traditionally, humans have relied on culture to provide them with a framework for addressing current problems. But as contemporary societies lose their traditional cultural knowledge, archaeology provides our best hope for deriving lessons from ancient cultures to address today’s problems. These articles report on our attempt to build a capability to study the past in ways that make it useful for thinking about our future.  While we may not be able to “wind back the tape of life” archaeology and agent-based modelling offer us new ways to understand human/environment interactions, providing us with a clearer picture of what may have occurred.”

These articles address such issues as the fragility of human existence in unstable environments, how humans can construct their own niches to better survive in these environments, and how small decisions can have dramatic effects to society.

With agent-based modeling still in its infancy in archaeology, this issue of Ecological Modelling should be of special interest to archaeologists, ecologists and modelers alike. It shows how important ABM can be to understanding archaeological systems, and reports fully on five distinct uses of ABM in archaeology. Importantly, it collates these studies into one easily digestible package, and allows for comparison of the different modeling approaches.

To read the articles:

Crabtree and Kohler, summary and intro

Rogers et al. on pastoral Mongolia

Murphy on Hohokam irrigation

Kohler et al. on Ancestral Puebloans and the Village Ecodynamics Project

Kirsch et al. on Hawaiian intensive agriculture

Barton et al. on Mediterranean environmental change

The full issue is here

–Stefani Crabtree


Buchanan, Mark

2009   Meltdown modeling: Could agent-based computer models prevent another financial crisis? (News Feature) Nature 460(6):680-682.

Cabrera, Derek, James T. Mandel, Jason P. Andras, and Marie L. Nydam

2008    What is the crisis? Defining and prioritizing the world’s most pressing problems. Frontiers in Ecology and the Environment 6(9):469–475.

Epstein, Joshua M.

2009   Modelling to Contain Pandemics: Agent-based computational models can capture irrational behaviour, complex social networks and global scale—all essential in confronting H1N1. (Opinion) Nature 460(6):687.

Gould, Stephen J.

1989   Wonderful Life: The Burgess Shale and the Nature of History.  W. W. Norton and Company. New York.

Computer Applications and Quantitative Methods in Archaeology conference

CAA is currently the largest annual conference focusing on computing in archaeology. It usually hosts a session about computational modelling and/or simulations but this year seems to be particularly prolific for complexity science. Here’s a quick tour of what we particularly look forward to:


(W12) Workshop: One hour, one model: Agent-based Modelling on-the-fly”

Organised by myself, Ben Davies, Tom Brughmans and Enrico Crema this workshop will aim at brining together researchers working with complexity science tools. We will divide into small groups and work in parallel on the most common building blocks of archaeological simulations (diffusion of an idea, innovation, environmental change etc) to see how different our approaches are and if different models could produce  different outcomes. We also hope to build a small library of code snippets.

(W11) Workshop: Introduction to network analysis for archaeologists

Run by Tom Brughmans, Ursula Brosseder and Bryan Miller it’s a half day hands-on workshop (so you can come to W12 as well) introducing the basic techniques of network analysis. No previous experience required.


(S25) Session:  Agents, Networks, Equations and Complexity: the potential and challenges of complex systems simulation

A full day session organised by the same team as the ‘One hour, one model’ workshop (Ben Davies, Iza Romanowska, Tom Brughmans, Enrico Crema). Last time I checked we had 18 papers in our session with presenters from all six continents and an enormous breath of applications, case studies and techniques. From Early Palaeolithic dispersals (that’s me! but also another paper by Dario Guiducci, Ariane Burke, James Steele which I’m really looking forward to) to Tierra del Fuego societies to  sea faring in Oceania to modelling 17th century Polish epidemics – you get 12 hours (!!!) of Agent-based Modelling, Network Analysis, Neural Networks and even a few theoretical papers. You can find the abstracts here:  S25. Agents, Networks, Equations and Complexity.

(S23) Session: Modelling approaches to investigate population dynamics and settlement patterns over the long term

Another giant session, thankfully not overlapping with S25. Focused on population  dynamics, settlement patterns and land use this session takes a leap forward from the traditional static, GIS approaches and looks for more dynamic modelling techniques such as simulation.

(S24) Session: Modelling approaches to study early humans in space and time

I had a pleasure to participate in this session at the CAA2013 in Perth and it was a fantastic combination of papers showcasing various techniques (databases, least-cost path analysis, ABM) used to approach the topic of mobility in prehistory.

(S20) Session: (Re)building past networks: archaeological science, GIS and network analysis 

Network analysis seems to be getting a strong hold in archaeological computing. This session shows a few of the most common applications (inter-visibility, transport/trade, connectivity of islands) as well as some new ideas.

Satellite Event: The Connected Past

On Saturday, the Connected Past team will hold a satellite conference on Network Analysis in archaeology. You can find their call for papers and all the details here: The Connected Past.

From the world of Complex Systems Simulation in Humanities