To prove that there is a world beyond agents, turtles and all things ABM, we have created a neat little tutorial in system dynamics implemented in Python.
Delivered by Xavier Rubio-Campillo and Jonas Alcaina just a few days ago at the annual Digital Humanities conference (this year held in the most wonderful of all cities – Krakow), it is tailored to humanities students so it does not require any previous experience in coding.
System dynamics is a type of mathematical or equation-based modelling. Archaeologists (with a few noble exceptions) have so far shunned from, what is often perceived as, ‘pure math’ mostly citing the ‘too simplistic’ argument when awful mathematics teacher trauma was probably the real reason. However, in many cases an ABM is a complete overkill when a simple system dynamics model would be well within one’s abilities. So give it a go if only to ‘dewizardify’* the equations.
People play video games, archaeologists included. People are spending more and more time in the virtual worlds presented by video games, raising the question of how our digital past is to be studied or curated. And video games are often constructed within historical frames, whether characters are fighting dysentery on the Oregon Trail or fighting mutants in a post-apocalyptic Boston. Video games offer a window into historical process and narrative-building that more passive media cannot.
There is a growing contingent of archaeologists and historians who are using and exploring video games as both media for portraying the past (or pasts), as well as a valuable source of information on the digital lives of humans in the more recent past. Greater historical detail in games also suggests a role for archaeologists in the development of games.
This ARCHON-GSA conference will explore the intersections of archaeology and video games. Its aim is to bring scholars and students from archaeology, history, heritage and museum studies together with game developers and designers. The program will allow for both in-depth treatment of the topic in the form of presentations, open discussion, as well as skill transference and the establishment of new ties between academia and the creative industry.
If you’re already going to be on the road for the CAA conference in Oslo, this conference conveniently begins right afterwards in Leiden. Abstracts are due on the 31st, and more information can be found here.
Looking at the stats page the other day, I noticed some traffic coming from an unfamiliar source:
Following the link, it turns out we’ve been nominated for an award! From the Digital Humanities community! Apparently they do that sort of thing! From the website:
Digital Humanities Awards are a set of annual awards where the public is able to nominate resources for the recognition of talent and expertise in the digital humanities community.
We’ve been nominated under the “Best DH Blog Post or Series of Posts” category. Luckily for us, nominees need only be “(vaguely)” in the realm of Digital Humanities, which is pretty much how we’ve always thought of ourselves. We’re certainly honored to be considered, and there are a lot of really interesting projects up here that are worth having a look at:
Photo from http://www.zentut.com/data-mining/data-mining-techniques/
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, “LongandspatiallyvariableNeolithicDemographic 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.
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.
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.
From the world of Complex Systems Simulation in Humanities