Tag Archives: agent-based modeling

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

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.