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.


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