Tag Archives: complex systems simulation

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:

http://jasss.soc.surrey.ac.uk/16/4/4.html

 

–Stefani Crabtree

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.

Student Conference on Complexity Science

Student Conference on Complexity Science run this year by the Institute for Complex Systems Simulation at the University of Southampton has just released an open call for sessions. Having participated in the last two editions I can happily vouch for it. Originally designed as a forum for the students of UK doctoral training centres in Complexity Science (Bristol, Southampton and Warwick) it grew to become a truly international and interdisciplinary event. It is one of those gatherings where pretty much everyone is out of their depth – 250 students spread through every imaginable field of science from the most hardcore maths to the soothing storytelling of humanities. The program looks exciting with the first day devoted to hands-on workshops, a few high-level keynotes and plenty of opportunities to drink coffee and wine and chat with likeminded PhD student. Not to mention Brighton is probably the ‘hipsterest’ of hipster towns. See you all there in August!

See their CfS below.

We are pleased to announce that the Call for Sessions at the 4th Student Conference on Complexity Science (SCCS2014) is now open.

The Conference

The Student Conference on Complexity Science (SCCS) is the largest UK conference for early-career researchers working under the interdisciplinary framework of Complex Systems with a particular focus

© Tessa Coe from the series 'complexity'. http://www.tessacoe.co.uk
© Tessa Coe from the series ‘complexity’. http://www.tessacoe.co.uk

on computational modelling, simulations and network analysis. Since 2010, this conference series has brought together PhD students and early career researchers from both the UK and overseas interested in areas ranging from quantum physics to the economics of happiness. The 4th Student Conference on Complexity Science will be held between 19-22 August 2014 at the University of Sussex, Brighton, UK.

If your work comes under the general topic of complexity science, we want to hear from you!

The Format
Green-on-yellow
© Tessa Coe from the series ‘complexity’. http://www.tessacoe.co.uk

The conference will consist of 4 hands-on workshops and 10 parallel sessions of 6-9 papers. We particularly encourage sessions crossing traditional disciplinary boundaries and/or dealing with the general theoretical underpinnings of complexity science. The general themes of the conference include but are not limited to:

  • Complex Systems Simulation in Physics, Engineering and Mathematics
  • Complexity Theory
  • Artificial Intelligence and Swarm Robotics
  • Network Science
  • Complexity Science applications in Earth and Social Sciences, Digital Economy and Digital Humanities
The Logistics

To submit a session please provide a short abstract and a list of potential speakers at http://sccs2014.soton.ac.uk/sessions.php. The list of chosen sessions will be announced on 17th February 2014.

We are pleased to announce that thanks to the generosity of the Institute for Complex Systems Simulation we will be able to offer a number of travel bursaries to the attendees.

The Deadlines
  • Call for Sessions closes: 12 p.m. (UTC) 27th January 2014
  • Sessions announced and Call for Papers opens: 17th February 2014
  • Call for Papers closes: 12 p.m. (UTC) 26th May 2014
  • Papers announced: 16th June 2014

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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.

Screen Shot 2013-05-11 at 21.08.23

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.