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Leave it to an archaeologist, but when I heard the CFP from Digital Humanities Now on ‘evaluating’ digital work, I immediately started thinking about typologies, about categorizing. If it is desirable to have criteria for evaluating DH work, then we should know roughly the different kinds of DH work, right? The criteria for determining ‘good’ or ‘relevant’, or other indications of value will probably be different, for different kinds of work.
In which case, I think there are at least two dimensions, though likely more, for creating typologies of DH work. The first – let’s call it the Owens dimension, in honour of Trevor’s post on the matter- extends along a continuum we could call ‘purpose’, from ‘discovery’ through to ‘justification’. In that vein I was mulling over the different kinds of digital archaeological work a few days ago. I decided that the closer to ‘discovery’ the work was, the more it fell within the worldview of the digital humanities.
The other dimension concerns computing skill/knowledge, and its explication. There are lots of level of skill in the digital humanities. Me, I can barely work Git or other command-line interventions, though I’m fairly useful at agent simulation in Netlogo. It’s not the kinds of skills here I am thinking about, but rather how well we fill in the blanks for others. There is so much tacit knowledge in the digital world. Read any tutorial, and there’s always some little bit that the author has left out because, well, isn’t that obvious? Do I really need to tell you that? I’m afraid the answer is yes. “Good” work on this dimension is work that provides an abundance of detail about how the work was done so that a complete neophyte can replicate it. This doesn’t mean that it has to be right there in the main body of the work – it could be in a detailed FAQ, a blog post, a stand alone site, a post at Digital Humanities Q&A, whatever.
For instance, I’ve recently decided to start a project that uses Neatline. Having put together a couple of Omeka sites before, and having played around with adding plugins, I found that (for me) the documentation supporting Neatline is quite robust. Nevertheless, I became (am still) stumped on the problem of the geoserver to serve up my georectified historical maps. Over the course of a few days, I discovered that since Geoserver is java-based, most website hosting companies charge a premium or monthly charge to host it. Not only that, it needs Apache Tomcat installed on the server first, to act as a ‘container’. I eventually found a site – Openshift - that would host all of this for free (! cost always being an issue for the one-man-band digital humanist), but this required me to install Ruby and Git on my machine, then to clone the repository to my own computer, then to drop a WAR file (as nasty as it sounds) into the webapps folder (but what is this? There are two separate webapp folders!) , then ‘commit, push’ everything back to openshift. Then I found some tutorials that were explicitly about putting Geoserver on Openshift, so I followed them to the letter…. turns out they’re out of date and a lot can change online quite quickly.
If you saw any of my tweets on Friday, you’ll appreciate how much time all of this took…. and at the end of the day, still nothing to show for it (though I did manage to delete the default html). Incidentally, Steve from Openshift saw my tweets and is coaching me through things, but still…
So: an importance axis for evaluating work in the digital humanities is explication. Since so much of what we do consists of linking together lots of disparate parts, we need to spell out how all the different bits fit together and what the neophyte needs to do to replicate what we’ve just done. (Incidentally, I’m not slagging the Neatline or Omeka folks; Wayne Graham and James Smithies have been brilliant in helping me out – thank you gentlemen!). The Programming Historian has an interesting workflow in this regard. The piece that Scott, Ian, and I put together on topic modelling was reviewed by folks who were definitely in the digital humanities world, but not necessarily well-versed in the skills that topic modeling requires. Their reviews, going over our step by step instructions, pointed out the many, many, places where we were blind to our assumptions about the target audience. If that tutorial has been useful to anyone, it’s entirely thanks to the reviewers, John Fink, Alan MacEachern, and Adam Crymble.
So, it’s late. But measure digital humanities work along these two axes, and I think you’ll have useful clustering in order to further ‘evaluate’ the work.
Is digital archaeology part of the digital humanities?
This isn’t to get into another who’s in/who’s out conversation. Rather, I was thinking about the ways archaeologists use computing in archaeology, and to what ends. The Computer Applications in Archaeology Conference has been publishing proceedings since 1973, or longer than I’ve been on this earth. Archaeologists have been running simulations, doing spatial analysis, clustering, imaging, geophysicing, 3d modeling, neutron activation analyzing, x-tent modeling , etc, for what seems like ages.
Surely, then, digital archaeologists are digital humanists too? Trevor Owens has a recent post that sheds useful light on the matter. Trevor draws attention to the purpose behind one’s use of computational power – generative discovery versus justification of an hypothesis. For Trevor, if we are using computational power to deform our texts, we are trying to see things in a new light, new juxtapositions, to spark new insight. Ramsay talks about this too in Reading Machines (2011: 33), discussing the work of Jerome McGann and Lisa Samuels. “Reading a poem backward is like viewing the face of a watch sideways – a way of unleashing the potentialities that altered perspectives may reveal”. This kind of reading of data (especially, but not necessarily, through digital manipulation), does not happen very much at all in archaeology. If ‘deformance’ is a key sign of the digital humanities, then digital archaeologists are not digital humanists. Trevor’s point isn’t to signal who’s in or who’s out, but rather to draw attention to the fact that:
When we separate out the the context of discovery and exploration from the context of justification we end up clarifying the terms of our conversation. There is a huge difference between “here is an interesting way of thinking about this” and “This evidence supports this claim.”
This, I think, is important in the wider conversation concerning how we evaluate digital scholarship. We’ve used computers in archaeology for decades to try to justify or otherwise connect our leaps of logic and faith, spanning the gap between our data and the stories we’d like to tell. A digital archaeology that sat within the digital humanities would worry less about that, and concentrate more on discovery and generation, of ‘interesting way[s] of thinking about this’.
In a paper on Roman social networks and the hinterland of the city of Rome, I once argued (long before I’d ever heard the term digital humanities) that we should stop using GIS displaying North at the top of the map, that this was hindering our ability to see patterns in our data. I turned the map sideways – and it sent a murmur through the conference room as east-west patterns, previously not apparent, became evident. This, I suppose, is an example of deformation. Hey! I’m a digital humanist! But other digital work that I’ve been doing does not fall under this rubric of ‘deformation’.
My Travellersim simulation for instance uses agent based modeling to generate territories, and predict likely interaction spheres, from distributions of survey data. In essence, I’m not exploring but trying to argue that the model accounts for patterns in the data. This is more in line with what digital archaeology often does.
Bill Caraher, I suspect, has been reading many of the same things I have been lately, and has been thinking along similar lines. In a post on archaeological glitch art Bill has been changing file extensions to fiddle about in the insides of images of archaeological maps, then looking at them again as images:
“The idea of these last three images is to combine computer code and human codes to transform our computer mediate image of archaeological reality in unpredictable ways. The process is remarkably similar to analyzing the site via the GIS where we take the “natural” landscape and transform it into a series of symbols, lines, and text. By manipulating the code that produces these images in both random and patterned ways, we manipulate the meaning of the image and the way in which these images communicate information to the viewer. We problematize the process and manifestation of mediating between the experienced landscape and its representation as archaeological data.”
In the same way, Trevor uses augmented reality smartphone translation apps set to translate Spanish text into English, but pointed at non Spanish texts. It’s a bit like Mark Sample’s Hacking the Accident, where he uses an automatic dictionary substitution scheme (n+7, a favorite of the Oulipo group) to throw up interesting juxtapositions. A deformative digital archaeology could follow these examples. Accordingly, here’s my latest experiment along these lines.
Let’s say we’re interested in the evolution of amphorae types in the Greco-Roman world. Let’s go to the Netlogo models library, and instead of building the ‘perfect’ archaeological model, let’s select one of their evolutionary models – Wilensky’s ‘Mimicry‘ model, which is about the evolution of Monarch and Viceroy butterflies swapping in ‘amphora’ for ‘moth’ everywhere in the code and supporting documentation, and ‘Greeks’ for ‘birds’.
In the original model code, we are told:
“Batesian mimicry is an evolutionary relationship in which a harmless species (the mimic) has evolved so that it looks very similar to a completely different species that isn’t harmless (the model). A classic example of Batesian mimicry is the similar appearance of monarch butterfly and viceroy moths. Monarchs and viceroys are unrelated species that are both colored similarly — bright orange with black patterns. Their colorations are so similar, in fact, that the two species are virtually indistinguishable from one another.
The classic explanation for this phenomenon is that monarchs taste desireable. Because monarchs eat milkweed, a plant full of toxins, they become essentially inedible to butterflies. Researchers have documented butterflies vomiting within minutes of eating monarch butterflies. The birds then remember the experience and avoid brightly colored orange butterfly/moth species. Viceroys, although perfectly edible, avoid predation if they are colored bright orange because birds can’t tell the difference.
This is what you get:
We have two types of amphorae here, which we are calling the ‘monarch’ type (type 1) and the ‘viceroy’ type (type 2).
This model simulates the evolution of monarchs and viceroys from distinguishable, differently colored types to indistinguishable mimics and models. At the simulation’s beginning there are 450 type 1s and type 2s distributed randomly across the world. The type 1s are all colored red, while the type 2s are all colored blue. They are also distinguishable (to the human observer only) by their shape: the letter “x” represents type 1s while the letter “o” represents type 2s. Seventy-five Greeks are also randomly distributed across the world.
When the model runs, the Greeks and amphorae move randomly across the world. When a Greek encounters a amphora it rejects the amphora, unless it has a memory that the amphora’s color is “desireable.” If a Greek consumes a monarch, it acquires a memory of the amphora’s color as desirable.
As amphorae are consumed, they are regenerated. Each turn, every amphora must pass two “tests” in order to reproduce. The first test is based on how many amphorae of that species already exist in the world. The carrying capacity of the world for each species is 225. The chances of regenerating are smaller the closer to 225 each population gets. The second test is simply a random test to keep regeneration in check (set to a 4% chance in this model). When a amphora does regenerate it either creates an offspring identical to itself or it creates a mutant. Mutant offspring are the same species but have a random color between blue and red, but ending in five (e.g. color equals 15, 25, 35, 45, 55, 65, 75, 85, 95, 105). Both monarchs and Viceroys have equal opportunities to regenerate mutants.
Greeks can remember up to MEMORY-SIZE desireable colors at a time. The default value is three. If a Greek has memories of three desireable colors and it encounters a monarch with a new desireable color, the Greek “forgets” its oldest memory and replaces it with the new one. Greeks also forget desireable colors after a certain amount of time.
And when we run the simulation? Well, we’ve decided that one kind of amphora is desireable, another kind is undesireable. The undesireable ones respond to (human) consumer pressure and change their color; over time they evolve to the same color. Obviously, we’re talking as if the amphorae themselves have agency. But why not? (and see Godsen, ‘What do objects want?’) That’s one interesting side effect of this deformation.
As I haven’t changed the code, so much as the labels, the original creator’s conclusions still seem apt:
Initially, the Greeks don’t have any memory, so both type 1 and type 2 are consumed equally. However, soon the Greeks “learn” that red is a desireable color and this protects most of the type 1s. As a result, the type 1 population makes a comeback toward carrying capacity while the type 2 population continues to decline. Notice also that as reproduction begins to replace consumed amphorae, some of the replacements are mutants and therefore randomly colored.
As the simulation progresses, Greeks continue to consume mostly amphorae that aren’t red. Occasionally, of course, a Greek “forgets” that red is desireable, but a forgetful Greek is immediately reminded when it consumes another red type 1. For the unlucky type 1 that did the reminding, being red was no advantage, but every other red amphora is safe from that Greek for a while longer. Type 1 (non-red) mutants are therefore apt to be consumed. Notice that throughout the simulation the average color of type 1 continues to be very close to its original value of 15. A few mutant type 1s are always being born with random colors, but they never become dominant, as they and their offspring have a slim chance for survival.
Meanwhile, as the simulation continues, type 2s continue to be consumed, but as enough time passes, the chances are good that some type 2s will give birth to red mutants. These amphorae and their offspring are likely to survive longer because they resemble the red type 1s. With a mutation rate of 5%, it is likely that their offspring will be red too. Soon most of the type 2 population is red. With its protected coloration, the type 2 population will return to carrying capacity.
The swapping of words makes for some interesting juxtapositions. ‘Protects’, from ‘consumption’? This kind of playful swapping is where the true potential of agent based modeling might lie, in its deformative capacity to make us look at our materials differently. Trying to simulate the past through ever more complicated models is a fool’s errand. A digital archaeology that sat in the digital humanities would use our computational power to force us to look at the materials differently, to think about them playfully, and to explore what these sometimes jarring deformations could mean.
Godsen, Chris. 2005. ‘What do objects want?’ Journal of Archaeological Method and Theory 12.3 DOI: 10.1007/s10816-005-6928-x
Ramsay, Stephen. 2011. Reading Machines. Towards An Algorithmic Criticism. U of Illinois Press.
Wilensky, U. (1997). NetLogo Mimicry model. http://ccl.northwestern.edu/netlogo/models/Mimicry. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
Finally, with a bit of space to breathe, I am turning to getting my HIST3812 Gaming and Simulation for Historians course put together. In response to student queries about what this course will explore, I’ve put together a wee comic book (to capture the aesthetic of playfulness about history that games & simulations naturally contain). I’m not a particularly good maker of comic books, but it does the trick, more or less.
See it on Issuu here
I was interviewed recently by a student in Leslie Madsen-Brooks graduate seminar in digital history, HannaLore Hein. She posts her impression of the interview on the course website here. It’s always interesting to see what you wrote come through someone else’s filters. Given a recent conversation on twitter, where Mike Widner and others have been discussing the results of text analysis/topic modeling on all of the posted interviews, I thought I’d post here the ur-text from our interview.
1. Did you begin your academic career wanting to be an archeologist? How did your studies as and undergraduate and graduate student lead you to your current career?
I grew up in a family with a very strong interest in history. My brothers and I all teach at various levels in the system, and various aunts & other family members all taught too. It was rather a given… as for archaeology, I was attracted by the materiality of it. I love historical landscapes. Archaeology forces you to confront that history happens in space and place, with and through objects. I like stuff. It was a good fit!
But it all comes down to an opportunity I had a at junior college in Quebec (a CEGEP, as they’re called). I had the opportunity to go to Greece on a study tour, and then to return the following year on an excavation. We worked on a medieval Cistercian abbey, in which was buried a mutilated skeleton. Its treatment was consistent with traditions surrounding vampires, so… it rather hooks you in, an experience like that!
I studied classical archaeology at Wilfrid Laurier University in Waterloo Ontario. I wasn’t very tech minded in those days, though I had had a C-64 growing up, and had programmed my own games in BASIC. I had an exercise in one class in 1995 where we were asked to go onto this “World Wide Web” and create an annotated webography of sites related to the Etruscans. Less than impressed with what I found, I wrote an essay entitled, ‘Why the World Wide Web Will Never Be Useful For Academics’.
My ability to predict the future is thus suspect.
2. Did you always have a knack for technology? Was it something that came easily to you, or something you really had to work at to understand?
I’ve been breaking things since I was 3. I took our family piano apart when I was ten, dropping all of the hammers and rendering a B-Flat completely useless ever since. In the sense that I’ve never been afraid to tinker, to try to understand how things work, then yes, you could say I have a knack for technology. With our C-64, I use to buy magazines that printed out all of the code for games, utilities, and so on. I did a lot of that sort of thing, down in the basement… but I’m always working hard to figure out how things work, and what I might use them for. I get a kick out of helping other people too. I believe in failing gloriously and failing often. It’s only through that cycle – and being willing to share what happened – that we move forward. Recently a project website of mine was hacked. I was gutted – I lost a summer’s worth of work. But on the flip side, it was a great moment to share with the wider community so that it wouldn’t happen to them. I posted about it here: http://electricarchaeology.ca/2012/05/18/how-i-lost-the-crowd-a-tale-of-sorrow-and-hope/ and was really heartened to see the comments of support (and tweets) about what went on.
Too often we only talk about things that worked just like we thought they would. We need to have a discourse about things we try that didn’t – and why.
3. What jobs have you held previously? Were there any skills that you acquired at those positions that you still use today?
My very first job was as a janitor, responsible for the washrooms at a summer resort. Being a janitor taught patience and fortitude in the face of really annoying ….stuff….. More to your question though, I’ve taught at all levels from High School through to Continuing Ed. Until I joined the faculty at Carleton, I worked in the world of for-profit online education. I learned a lot about teaching and tech in those positions. I was a free lance heritage consultant at one point, with a couple of government contracts, where mission creep is a very real issue. Learn to say no, learn to draw the line. I also have a business with my family in what could be considered the heritage agritourism field. Again though I consider that a form of teaching – understanding customers, understanding students, can be very similar. That’s not to say that students are customers, mind you. Paying for tuition is like paying for ice time- it gets you on the ice, I’ll coach you, but you don’t necessarily get to hoist the Stanley Cup.
4. How advanced is your knowledge of computer science and programing? Is that a major component of your job?
I’m always reading, always learning. Talk to the comp.sci folks. Keeping up with what’s going on, and trying to identify which skills are the ones I need. There’s a lot to recommend just playing and tinkering though, in terms of teaching. When you are formally taught something, you tend to internalize that particular mode of doing whatever it is. I’m sure there are probably more effective ways of learning the skills I need, but this is what seems to work for me. I’ve heard of people getting credit towards tenure for ‘learning python’ or what have you, so that’s encouraging. Works like The Programming Historian are a fantastic resource, and I’m continually astounded by what other folks can do. I’m really a bit of a fraud. First day in the department, I couldn’t find the on switch for the Macs…. (I’m a pc guy).
5. What is your favorite form of digital communication? (Blogs, Twitter, etc.) What form do you think is most respected in the field? What form is the most “academically accepted?”
I have worked hard on my blog, from 2006 onwards, to make it a useful form of academic output for me. I thank Alan Liu and other participants at the 1st Nebraska Digital Humanities Workshop (were I’d been invited to present) for pushing me to blog. Once I started giving it away on my blog, I started getting traction in academia (that I wasn’t getting as a Romanist). A careful, thoughtful blog is a sinequanon for the digital humanist, as is a twitter account. I don’t care much for Facebook or Google +. In terms of ‘academically accepted’, I can show you structural reasons why blogs matter in terms of speaking beyond and to the academy. Someone has to generate the content on the internet, right? Experiments like the Journal of Digital Humanities and things like the LSE Impact Blog are slowly securing the short-form quick-publish genre as an accepted format of scholarly output. Blogging is platform, not genre. We shouldn’t confuse the two. In some senses, the journal article or monography is the last stage of the process, an archive rather than a picture of developing scholarly output. That’s going to be the biggest change.
6. How do you balance your career/projects between the digital and traditional academic worlds?
Happily, I’m one of the first people in Canada to have ‘Digital Humanities’ as my job title, so I’m making it up as I go along.
7. I noticed on your blog that you cite extensively. Is that common practice among digital humanists?
Blogging as a platform has nothing to say about citations. I cite, because I want to give credit and to show where my original thought begins. It’s pretty common on academic blogs. Linking is a form of citation too.
If there is any other information that you think is pertinent to the field of digital humanities, especially in relation to public history, that I did not touch upon in my questions, I would love to hear your thoughts.
All digital history is public history, far as I’m concerned. Working online allows an interested public to become part of the project. Precious few have read my book; about a hundred people a day take a look at my blog.
One of the things I want my students to engage with in my ‘cities and countryside in antiquity’ class is the idea that in antiquity, one navigates space not with a two dimensional top-down mental map, but rather as a series of what-comes-next. That navigating required socializing, asking directions, paying attention to landmarks. I’m in part inspired by R. Ling’s 1990 article, Stranger in Town, and in part by Elijah Meek’s and Walter Scheidel’s ORBIS project. Elijah and I have in fact been talking about marrying a text-based interface for Orbis for this very reason.
But I’m also interested in gaming, simulation and storytelling for their own merits, so I’m trying my hand at an interactive fiction written using Inform 7 along the same lines. Instead of interfacing directly with the model represented in Orbis, I’ve queried Orbis for travel data, and have begun to write a bit of a narrative around it. (One could’ve composed this in Latin, in which case you’d get not just the spatial ideas, but also the language learning too!).
Anyway, I present to you version 0.1, a beta (perhaps ‘alpha’ is more appropriate) for ‘Stranger in These Parts‘, by Shawn Graham. I’m using Playfic to host it. I’d be happy to hear your thoughts. (And a hint to get going: check to see what you’ve got on you, and ‘ask Eros’ about things…)
Obviously, some things are lacking at the moment. I’ll want the player to be able to select different modes of transport sometimes (and thus to skip settings). There’s a point system, but it’s meant more to signal to the students that there is more to find. Depending on which NPCs a student talks with, different kinds of routes should become available. Time passes within the IF, and so night time matters – no travel then. As far as I know, there’s no such thing as multi-player IF or head-to-head IF, but that’d be fun if it were possible: can you get to Pompeii before your classmates?
In terms of the learning exercise, the students will play through this, and then explore the same territory in Orbis. In the light of their readings and experiences, I’ll be asking them to reflect on the Roman experience of space. Once we’ve done that, now being suitably disabused of 21st century views of how to navigate space, we’ll start looking at the landscape archaeology of other ancient cultures.
That’s the plan, at any rate.
Below is a draft of my syllabus for my upcoming class on ‘Cities and Countryside in the Ancient World’ class. I’m very Mediterranean-centric; 12 weeks won’t allow for much else, and stick with what you know, right? Comments, suggestions are welcome.
HIST3902-A Cities and Countryside in the Ancient World
Cities are creatures of the countryside. Understanding that relationship is key to understanding the ancient world. Discuss.
This course looks at the relationship between cities and countryside in the ancient world, as evidenced primarily through landscape archaeology. I will be arguing, amongst other things, that the form of that relationship is the key indicator for understanding the mindset, the nature of, that particular culture. It is no accident that ‘cities’ and ‘civilization’ are etymologically related: thus, looking at cities and countryside will give us an understanding of what being civilized meant in antiquity.
Hacking as a Way of Knowing
Every exercise in this course builds on every other, as we build tools and work with data to construct an understanding of what it meant to be civilized in the (Greco-Roman) ancient world. The course objectives then are to:
- Introduce and explore the study of ancient landscapes, society, and economy
- Develop facility with representing archaeological and historical data using GIS and/or Network Analysis
- Make a positive and public contribution to scholarly knowledge on some aspect of Greco-Roman antiquity as it played out across space.
You will be working with datasets that have been made available to you by scholars working in the field. I am enormously grateful to these partners. Some of this material is unpublished; all of it is rich. You have the opportunity to make real contributions to scholarly knowledge by mining and analyzing this data for new insights. Accordingly, you must maintain the highest standards of professionalism and academic ‘good citizenship’ as you work with this data.
I do not see the point of assigning you work that only I or the TA will read. In which case, we will be conducting certain portions of this course in public on the internet. (For more on my teaching philosophy, see http://bit.ly/LLq765).
Anything posted online may be posted under a pseudonym should you have privacy concerns. You need to discuss these with me during week 1. I strongly recommend you do use your own name, so that you can begin to build your online footprint as a serious scholar. I also suggest you begin to lurk on Twitter, to follow prominent archaeologists and historians there and on Academia.edu, so that you can connect to a world-wide community of practice.
The best student work will be posted and promoted on Electric Archaeology (electricarchaeology.ca) and on Twitter, with the ambition of having the Journal of Digital Humanities select it for formal publication. If your work is selected, you are under no obligation to have your work promoted in this fashion should you so choose.
We will discuss and explore a number of key concepts. Terms that you should watch out for in your readings: primitivist, modernist, consumer city, producer city, bazaar, space syntax, actor-network theory, social network analysis, landscape formation processes, the anthropological nature of time/space, networks, information systems, agent-based simulation, computational economics.
The following is on reserve in the library. Its philosophical and methodological approach will underpin much of what we will do.
Knappett, Carl. An Archaeology of Interaction: Network Perspectives on Material Culture and Society. Oxford University Press: Oxford, 2011.
Demonstrating Your Scholarly Growth
- ORBIS and the social experience of space – due in class Wednesday Week 2 (September 19). 10%
- GIS/Network Analysis – due in class Wednesday Week 9 (November 7). 30%
- Final Project – due in class Monday December 03. 40%
These three assignments dovetail into one another. The first exercise involves working with ORBIS The Stanford Geospatial Network Model of the Roman World http://orbis.stanford.edu/ to experience, via networks, maps, and simulation, something of the social experience of space around the Mediterranean. In the second exercise, you will create maps and/or social network graphs from real data provided by our partners (exact details TBA). In the Final Project, you will combine your understanding of the spatial realities of the ancient world with your maps and graphs in a final project which may combine media and text to answer the question with which we began the class.
- Theory & Practise Exercises – due at end of Week 4 (October 5) and Week 6 (October 19). 20%.
These are a suite of exercises you may redo until you have achieved mastery. You may begin these exercises during Week 2, and submit at any point prior to the due date. The earlier you submit, the greater the chance that we can look at the work and help you. You have to allow at least 4 days for us to look the work over and return it to you. If you submit 4 days before the due date, you will not be allowed to redo the work. Please keep in mind that by offering you this chance, we are accepting a heavy grading load, and we ask for courtesy as we do so.
NB You will note that there is no final exam. DO NOT take that as a sign that this class is not as important as your other classes. By not having a final, I wish to signal to you that you must bring your best work to bear on your class work at all times.
Required (free) Software
You should download and install the following free software packages on your computer – or team up with someone else who can download and install them, should you not have access to a suitable machine. Note that ‘Portable GIS’ is meant to be run from a USB stick, and thus could be run on University computers.
Some of the following simulations can be run in your browser (others you may only read about, as the code hasn’t been released). They (and their associated texts) should be explored. Why do they work the way they do? What are the assumptions behind them? How do they enhance or not your understanding?
Roman Itineraries http://graeworks.net/abm/itineraries.html
Procedural Modeling of Cities http://ccl.northwestern.edu/cities/
Artificial Anasazi (a key archaeological implementation of agent based modeling) http://www.openabm.org/model/2222/version/1/view
Timelines & Mapping resources
Introductory Lecture on Netlogo – Agents in Archaeology (video)
GIS and Agent-Based Modeling http://gisagents.blogspot.ca/
Archaeological Networks http://archaeologicalnetworks.wordpress.com/
The Scottbot Irregular http://www.scottbot.net/HIAL/
Maps, Data and Government Information Centre http://www.library.carleton.ca/contact/service-points/madgic
Digital Humanities Subject Guide http://www.library.carleton.ca/research/subject-guides/digital-humanities#welcome
Greek and Roman Studies Subject Guide http://www.library.carleton.ca/research/subject-guides/greek-and-roman-studies
The following weekly schedule of topics is tentative and subject to change.
Part 1 (September):
- Setting the scene: history & theory. Abandoning your 2-dimensional, top-down view of space: the emic vs. the etic.
- Minoans, Mycenaeans and the Aegean
- The Era of Colonization (Greeks, Etruscans, Phoenicians)
Part 2 (October):
- Maps and GIS
- Agent Based Simulation
- Network Analysis
- Landscape Archaeology & Survey
Part 3 (November):
- Greek Cities
- Greek Landscapes
- Roman Cities
- Roman Landscapes
The End (December 3)
- Answering the questions with which we began.
The following readings are indicative of the issues involved, and will deepen your understanding. This list is by no means exhaustive – consult the works’ bibliographies to pointers to further work! I provide them here to help you, to round out the ideas presented in our meetings. You will be able to make useful contributions to that discussion if you come prepared, having looked at some of these works. If you can’t find them on your own, you *must* ask the Historian/Classics Librarian for help to find other possible books/articles/online resources that can speak to the week’s topic. I expect you to read beyond the works listed here. Do you know how to use Google Scholar? Have you ever used L’Année philologique?
Agar, M. (2003). ‘My kingdom for a function: modelling misadventures of the innumerate’, Journal of Artificial Societies and Social Simulation 6.3. http://jasss.soc.surrey.ac.uk/6/3/8.html
Bang, P., Mamoru Ikeguchi and Harmut G. Ziche, eds. (2006). Ancient Economies, Modern Methodologies : Archaeology, Comparative History, Models and Institutions.
Brughmans, T. (2012). ‘Thinking through networks: a review of formal network methods in archaeology’, Journal of Archaeological Method and Theory 19.2 Online version: DOI: 10.1007/s10816-012-9133-8 http://www.springerlink.com/index/10.1007/s10816-012-9133-8
Conolly, J., and M. Lake. (2006). Geographical Information Systems In Archaeology.
Coward, F. (2010). ‘Small worlds, material culture and Near Eastern social networks’, Proceedings of the British Academy 158, 449-479. http://www.fcoward.co.uk/Cowardsmallworlds.pdf
Frier, B. and D. Kehoe. (2007). ‘Law and economic institutions’, in W. Scheidel, I. Morris, R. Saller (eds.), The Cambridge Economic History of the Greco-Roman World. 113-143.
Graham, S. (2006). ‘Networks, Agent-Based Modeling, and the Antonine Itineraries’, The Journal of Mediterranean Archaeology 19.1: 45-64.
Graham, S. (2009) ‘The Space Between: The Geography of Social Networks in the Tiber Valley’ in Coarelli, F. and Patterson, H. (eds) Mercator Placidissimus: the TiberValley in Antiquity. New research in the upper and middle river valley.
Graham, S. and J. Steiner. (2008). ‘Travellersim: Growing Settlement Structures and Territories with Agent-Based Modelling’, in J. Clark and E. Hagemeister (eds.), Digital Discovery: Exploring New Frontiers in Human Heritage. CAA 2006. Computer Applications and Quantitative Methods in Archaeology. Proceedings of the 34th Conference, Fargo, United States, April 2006. 57-67.
Ingold, T. (1993). ‘The Temporality of the Landscape’. World Archaeology 25.2 152–174.
Johnson, M. (1999) Archaeological Theory.
Lansing, J. S., and J. N. Kremer. (1993). ‘Emergent Properties of Balinese Water Temple Networks: Coadaptation on a Rugged Fitness Landscape’. American Anthropologist 95.1: 97–114.
Laurence, R. (2001). ‘The Creation of Geography: An Interpretation of Roman Britain’ in C. Adams and R. Laurence (eds.). Travel and Geography in the Roman Empire.
Massey, D. J. Allen, S. Pile (eds.) City Worlds: Understanding Cities 1.
Neville, M. (1996) Metropolis and Hinterland : the City of Rome and the Italian Economy, 200 B.C.-A.D. 200.
Orejas, Almudena, and F. Javier Sánchez-Palencia. (2002). ‘Mines, Territorial Organization, and Social Structure in Roman Iberia: Carthago Noua and the Peninsular Northwest’. American Journal of Archaeology 106.44: 581–599.
Pettegrew, D. K. (2007). ‘The Busy Countryside of Late Roman Corinth: Interpreting Ceramic Data Produced by Regional Archaeological Surveys’. Hesperia: The Journal of the American School of Classical Studies at Athens 76.4: 743–784.
Schortman, E. and W. Ashmore. (2012). ‘History, networks, and the quest for power: ancient political competition in the Lower Motagua Valley, Guatemala’, Journal of the Royal Anthropological Institute 18.1: 1-21.
Smith, M. (2005) ‘Networks, Territories, and the Cartography of Ancient States’. Annals of the Association of American Geographers 95.4: 832–849.
I’m teaching HIST3902A: Cities and Countryside in the Ancient World next fall. I would like very much for my students’ project work in this class to actually contribute to the creation of knowledge. Do you have an archaeological dataset (GIS or networks) that you wouldn’t mind sharing or having some neophytes work with? If so, please do get in touch. These are history students, not archaeology students, so there will be a steep learning curve.
Course Blurb: This course looks at the relationship between cities and countryside in the ancient world, as evidenced through archaeology and field survey (primarily Greece and Rome, though we might look at other cultures such as the Etruscans). I will be arguing, amongst other things, that the form of that relationship is the key indicator for understanding the mindset, the nature of, that particular culture. What do extra-urban sanctuaries really mean? How do cities warp the economic and cultural geography of a region? What does the idea of countryside mean to the Romans, the Greeks?
We will be using GIS and other digital tools to explore and understand that mindset. Assignments will be crafted around the idea of building knowledge online. Readings will be culled from journals and archaeological project websites.
I’m contributing to a volume on ‘Land and Natural Resources in the Roman World’. Below is my draft, on which I welcome comments and questions.
Towards the computational study of the Roman economy
Shawn Graham, Carleton University, Ottawa Canada
“Economies are complicated systems encompassing micro behaviours, interaction patterns, and global regularities. Whether partial or general in scope, studies of economic systems must consider how to handle difficult real-world aspects such as asymmetric information, imperfect competition, strategic interaction, collective learning, and the possibility of multiple equilibria. Recent advances in analytical and computational tools are permitting new approaches to the quantitative study of these aspects. One such approach is Agent-based Computational Economics (ACE), the computational study of economic processes modelled as dynamic systems of interacting agents.”
Most models of the Roman economy do not take into account this idea that micro-behaviours, feedback, and local interaction provide the circumstances out of which emerge those larger issues about which we are typically concerned. Before we can ask questions about growth, or market integration, or the degree to which Rome was ‘primitive’ versus ‘modern’, we have to focus on individual decision making. In this paper, I outline an agenda for how we might be able to do this. It comes down to this: we have to draw out and understand networks of individuals at all geographical scales and then use those networks as the substrate for computationally simulating individuals’ economic activities. Thus, archaeology and ancient literature become united through computation.
It is not necessary, I think, to rehash the historiography of studies of the Roman economy; the broad outlines of the debate are well known. What I find exciting though is the emergence of the New Institutional Economics of Douglass North. These works all draw attention to ideas around the consequences of individual decision making from incomplete knowledge, of how ‘good enough’ or satisficing decisions push actors and economics towards path-dependence (where because of past decisions one is locked into a particular mode). The idea that network relationships (and the institutions that emerge to promote these) are the mechanism through which ancient economies deal with incomplete knowledge is a powerful one because we can find and outline the traces of these networks through archaeology.
Bang takes this idea further and through cross-cultural comparison with Mughal India pushes our attention to the bazaar: “a stable and complex business environment characterised by uncertainty, unpredictability and local segmentation of markets’. Bang shows how the bazaar helped shield the individual from (while at the same time encouraging) fragmented information and instability. For Bang, one of the key mechanisms for exploring and understanding a bazaar-like economy is social networks, the way they form, and how information flows through them. We need to focus on the social differences between actors in a market situation.
Even the Emperor can be incorporated into such a perspective. In considering the Emperor as both the embodiment of the state and a private person intervening in its economy as one more player amongst other private citizens, Lo Cascio draws attention to the interplay between the Emperor’s euergetism and private markets, that he solved the problem of feeding Rome “by leaving unchanged the market mechanisms at work” and by “establishing and enforcing the rules of the game”. He does so to ensure that individuals neither engage in rampant speculation to force up prices, but also to ensure that prices do not become set to low, that is, to ensure an adequate profit. The success or failure of the Emperor to do this should depend on his position in the network.
This is a very complex view, too complex for any one individual to hold in one’s head and to be able to understand the non-linear outcomes of so many interacting parts. We need the computer. To understand the Roman economy, we need to simulate it. The best way to do this is to ground our simulation in what our evidence actually gives us: the actions of individuals in the past, whether we find that evidence in the archaeology or the ancient literature. We have to build up from individuals before we can begin to understand what ‘the Roman economy’ might actually mean.
Roman Economics Needs to be Networked
Networks and network analysis are currently in vogue in many areas of research. Tom Brughmans has neatly summarised the historiography and main issues surrounding networks and network analyses as applied to archaeological research. In essence, he argues that all of the varied approaches to network analyses are united by the idea that networks are ubiquitous and influence decision making; through networks both tangible objects and intangible influences spread (and in turn, promote or hinder network growth). Through studying networks, we are able to bridge the study of parts through reductionism to the emergent whole. The methodological advantages enumerated by Carl Knappett can be tied explicitly to the advantages that the New Institutional Economics bring. A network perspective forces one to consider relations between entities; they are spatial whether considered in social or physical terms; they work across scales; they can incorporate people, objects, and time.
Schortman and Ashmore make the argument that a by-product of social networks is the emergence of power, through collaborative or cooperative action. Debates around structure and agency in the social sciences revolve around questions related to the concentration of power and the generation of hierarchies across multiple spatial scales. It is from this contending for assets that politics becomes necessary and structures for the same emerge. 
Achieving power over others involves monopolizing some aspect of the production, distribution or use of materials. On the other hand, others can contest this by using their own social networks to gather resources (whether material or social). In this way, action through social networks results in political structures which are themselves the aggregate of flows of materials and ideas through social networks.
Perhaps the most well-known term in network studies is the idea of the ‘small-world’, first coined by Stanley Milgram. A small-world is not just a metaphor, but rather a precise concept in network terms, where a randomly connected network with mostly local connections has a few long distance connections which allow the entire network to be spanned in only a few steps. This is a crucial concept and one we should look for archaeologically or as a by-product of our models. Brughmans writes
“ … Such a specific topology has direct implications for the processes underlying it, like the transportation of materials, the spread of religious ideas or the enforcement of political power. These processes would largely take place between the highly connected nodes [actors of whatever kind] and they would only reach the larger number of less connected nodes through these [linkages]. In a small-world network, on the other hand, nodes within the same small-world are more often directly connected to each other, while only processes involving other small-worlds (e.g. long-distance trade) would go over the bridging nodes.”
How can we draw out networks from archaeological materials? Objects carry the resonances of who make them. They mark out membership and social identity. “Exchanging these items thereby manifests and extends crucial social linkages… Networks, therefore, come alive, in part, through the transfer of items that partake of their members’ social essences”. Objects therefore could be taken as proxies for social actors. Artefacts are the result of human, individual, decisions. They influenced other individuals at the time through their complex resonances of thing and place and object life-history. By considering artefacts and their relationships explicitly in terms of social network analysis, we reconnect with the individual in the past, and we obtain a perspective that allows us to see what kinds of actions were possible in the past, patterns of agency and structure, that those actors themselves could not see.
Anytime one can discern a relationship, a network is possible. Fiona Coward reminds us that the archaeological record is not a passive by-product but rather is in fact social relationships: “The patterning of material culture is a direct result of the social relationships between individuals and groups in which these objects were caught up. A network perspective provides a much more realistic picture, not only of objective sociality, but also potentially of individuals’ subjective experience of their worlds”.
But as Scott Weingart warns us, we also have to take into account the dangers of methodology appropriation. Network analysis comes to us from graph theory, from statistics, from computer science. The methods, philosophies and concerns of those disciplines are not necessarily congruent with archaeology. Brughman’s recent work will go a long way to addressing the potentials and perils of drawing out networks from archaeological materials.
If we can draw out networks from the ancient literature or the archaeological record, what then? We re-animate these networks using agent based (or ‘individual based’) modelling. This is a technique where a population of autonomous, heterogeneous ‘agents’ are created within a computerized environment. They are given rules of behaviour which they implement given a particular situation (whether when interacting with other agents, or with the environment). They are goal-oriented and to a degree, self-aware. That is, if we are interested in something called ‘the Roman economy’, we simulate the agents that compose that economy and their behaviours: not the economy itself (in contrast to traditional economic models with their equations and ‘rationalizing’ assumptions; we simulate at one level of complexity below our ‘target’.
Lea Tesfatsion discusses a number of ways in which ‘agent-based computational economics’ can be used, and classifies different studies according to their objectives. The one which should concern us here is what she calls, ‘qualitative insight and theory generation’: “how can economic systems be more fully understood through a systematic examination of their potential dynamical behaviours under alternatively specified initial conditions?” A key feature of agent modelling that differentiates it from other approaches is that once the starting conditions are specified, all subsequent events are driven by agent interactions. The researcher then is not so concerned with the final results of the simulation as its evolution over time, its history. Thus, the focus is on the process. That is to say, we study the model’s history to generate insight into real history.
Research into agent based models of economies has found that the topology of interactions matters. It is not just the pattern of social ties that matters, but also the environment in which these actions take place. We can explore the environmental aspect by setting the simulation on top of a cellular-automaton, a chess-board like arrangement of squares, where each square represents a unit of land and its holdings, and which responds to rules about growth, climate, geology, and so on. In many ways, a cellular automaton represents a dynamic geographic information system and which could obviously be drawn from archaeological GIS. Combined with an agent-based model representing the decision making agents, we then have a powerful tool.
A number of researchers have applied ABM and cellular automata to studying problems of structural change in agricultural societies, exploring everything from government intervention, to the diffusion of innovation in agriculture, to the beginning of European-style agriculture in Indiana in the early 1800s. These studies point to ways in which economic path-dependence emerges, under what circumstances innovation may diffuse, and decision making processes at different spatial scales.
Archaeology and Simulation
Simulation has a long history in archaeology. Agent based modelling is an outgrowth from several different fields, primarily game theory and complex systems studies. John Barret, in considering the relationship between agents and society, drew attention to how agents both form and are constrained by, the social structures that emerge from their interactions. He argued therefore that the level of ‘society’ should not form the basic unit of archaeological analysis, but rather the individual. It is social learning that creates a society (or an economy, for that matter).
There are now many agent-based models of past societies. Many of these are quite complicated, with many moving parts, which can make it difficult to understand what the model results may actually be telling us. I advocate instead for extremely simple models, exploring only a limited aspect of the phenomena in which we are interested in. After building a series of these, we can consider their results in aggregate.
Building a Simulation
How do we translate our arguments over the Roman economy into an agent framework? Tesfatsion suggests a four-step method for recasting that understanding in a way that can be modelled and explored:
“• Select as your benchmark case an equilibrium modelling of an economy from the economic literature that is clearly and completely presented and that addresses some issue you care about.
• Remove from this economic model every assumption that entails the external imposition of an equilibrium condition (e.g., market clearing assumptions, correct expectations assumptions, and so forth).
• Dynamically complete the economic model by the introduction of production, pricing, and trade processes driven solely by interactions among the agents actually residing within the model. These procurement processes should be both feasible for the agents to carry out under realistic information limitations and appropriate for the types of goods, services, and financial assets that the agents produce and exchange.
• Define an “equilibrium” for the resulting dynamically complete economic model.”
Tesfatsion remarks that when she tries this exercise with economics students, they find it difficult to understand the economy at this level working with individual agents. The challenge of this method is that it foregrounds survival: that the needs of subsistence, of surviving over time (death is always a possibility in these models) are the bedrock on which everything else is based.
Thus, we build our rule-sets that our computational agents will follow from our understanding of how an individual [Roman; collegium; military unit; family; city] acts in particular situations. While every agent might have the same suite of variables, each agent is heterogeneous. Its particular combination of variables is unique. We might all be playing basketball by the same rules, but my abilities are different than yours. We model the appropriate underlying environment. We specify the initial starting conditions, and then set the simulation in motion. We run the simulation over and over again to explore the complete behaviour space for all of the particular starting conditions, to see what emerges when and how.
To know that we have found something new about the Roman economy from such a process, I would suggest that we ground one of the model’s behaviours in social networks. There are various ways this might be accomplished. We might draw from an understanding of how patronage worked in the Roman world. Or, we could draw from Bang’s arguments about Rome-as-bazaar. Then, when the simulation has run its course, we can measure the emergent network and compare its features to real networks known from the archaeology. When the two correspond, then the model settings for that particular run have something important to tell us about ancient society. Alternatively, we could reverse that and specify networks found in the archaeology as our starting point. Do the model outcomes, when set from a starting point known archaeologically, make any sense according to what we believe to have been true about the Roman economy? If not, then perhaps our rule sets are flawed.
Doran underlines some of the main impediments to simulation building amongst archaeologists, limitations in computing power, and the disciplinary boundaries that create barriers to knowledge building. As Doran points out, the computing power issue is largely no longer an issue. Quite complicated simulations may be built on a desktop computer without too much trouble in terms of hardware. The second problem is more difficult. One way we can break those boundaries down may lie in the choice of simulation environment: the simpler and more intuitive the framework, the easier to use, develop, and communicate results. In my own work, I use the open-source Netlogo modelling environment, which I recommend to anyone interested in exploring the possibilities of this approach. While Netlogo has its genesis in efforts to educated school children in complex systems thinking, it is now in its fifth major release and is quite powerful. The learning curve is not overly steep, and much can be accomplished through tweaking the many models that come pre-packaged with the software (more on this below).
A simulation is an argument in computer code about the way the world works, and so represents a kind of ‘procedural rhetoric’. It is object-oriented, meaning that each individual behaviour exists as its own object. One then arranges the objects (which can be conditional) in the order the agents should carry them out, given a particular position in the environment or social position vis-à-vis other agents. One can have quite sophisticated simulations running after a day of working through the included tutorials. This should not be taken as a sign that Netlogo is simplistic. Quite powerful models have been built in it, including modelling the emergence of cities in the third millennium BC.
An excellent place to begin is with the included model, ‘Wealth Distribution’. In this model, a ‘world’ is simulated where grain is distributed randomly. In some places it grows thick on the ground; in other places it is sparse. A population of agents are introduced to this world. Their goal is to find enough grain to keep living, and to reproduce when conditions are right. The agents have ‘vision’ or knowledge of the world, to differing degrees. They have ‘metabolism’, or a preset amount of food they must consume with each time-step or they shall die (which again varies by individual). The amount of food collected above this metabolic rate becomes ‘wealth’. When this simulation is run, it becomes apparent that the differential distribution of resources in this world is sufficient in itself to create a partition of the world into classes where there are a few extremely wealthy individuals and a vast mass of others who are in constant danger of ‘dying’ from not having enough food.
We could then extend this model to represent something of the Roman world. We could give the agents a way of looking for help, of becoming a client of someone a bit wealthier than themselves. In return we could imagine that these ‘clients’ could offer support to their patron in turn. Perhaps the number of followers, and their relative ‘wealth’, could be translated into a score for ‘prestige’ which in turn affects the ‘patron’s’ ability to extract wealth from the world. What kind of artificial society results? In my ‘Patronworld’ model, which had its inspiration in the Wealth Distribution model, chains of connected individuals (that is, networks) do emerge from this dynamic, but they are fragile. One result seems to be that extremely high levels of gift-giving seem to go hand in hand with network collapse. It seems to do this by destabilizing the networks: too many people outside particular chains of patrons-clients are shut out of the system. Given the competitive building that characterizes the late Republic, this result is intriguing. It is a very simple model, to be sure, but one that foregrounds an important element of new models of the Roman economy: networks and social life. This kind of modelling also has the virtue that if one disagrees with the assumptions of the model, the code can be easily modified and adapted. In this way, model building is not an end point of research but rather a first step of a larger conversation (my own models may be found at the digital data repository Figshare.com and I welcome their use, adaptation, and improvement).
We need more and better networks drawn from historical and archaeological data. It is not enough, in contrast to Malkin, to use ‘networks’ as simple metaphor. Particular network topologies have different implications for the actors which make them up. If we are to make progress on the Roman economy, we need to explore the multiple networks of individuals and objects at multiple social and spatial scales. We need to turn our archaeological geographic information systems into computing environments in which agents can interact: at a stroke, we will have unified landscape archaeology, ancient history, and the study of ancient economics. Once we have this data, we can take our current understandings of the ancient economy, whether ’consumer city’, ‘primitive’; ‘modernising’, ‘bazaar’, NIE, or something else and translate them into an agent based simulation. If we can generate analogous networks to the ones we know archaeologically, then we might just have the wherewithal to argue that we have a model that tells us something useful, something new, about the past.
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Symons, S. and D. Raine. (2008) ‘Agent-Based Models Of Ancient Egypt’, in N. Strudwick (ed.), Proceedings of Informatique et Égyptologie. Piscataway, NJ. http://www.physics.le.ac.uk/ComplexSystems/papers/AgentBasedModelsEgypt2008.pdf (accessed 28/5/12).
Tesfatsion, L. (2006). ‘Agent-based computational economics: a constructive approach to economic theory’, in L. Tesfatsion and K. Judd (eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics. Amsterdam, 831-880.
Vriend, N. (2006). ‘ACE models of endogenous interactions’, in L. Tesfatsion and K. Judd (eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics. Amsterdam, 1047-1080.
Wallace-Hadrill, A. (1989) (ed.) Patronage in Ancient Society. London.
Watts, D. (1999). Small Worlds. The Dynamics of Network Between Order and Randomness Princeton.
Watts , D., and S. Strogatz. (1998). ‘Collective dynamics of ‘small-world’ networks’, Nature 393: 440-42.
Weingart, S. (2012). ‘Demystifying networks’, the scottbot irregular http://www.scottbot.net/HIAL/?p=6279 (accessed 28/5/12).
Wilensky, U., and M. Resnick. (1998). ‘Thinking in Levels: A Dynamic Systems Approach to Making Sense of the World’ Journal of Science Education and Technology 8.1: 3-18.
Wilensky, U. (1999). Netlogo . Evanston, IL. http://ccl.northwestern.edu/netlogo (accessed 28/5/12).
Wilhite, A. (2006). ‘Economic activity on fixed networks’, in L. Tesfatsion and K. Judd (eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics. Amsterdam, 1013-1046.
 Tesfatsion 2006: 832.
 Indeed, many economic models fail to recognize that the spatial context of economic action undermines many of the basic assumptions of economic theory, Dibble 2006: 1515.
 The discussion is usefully treated in for example Scheidel 2010, Manning and Morris 2005, and Scheidel and von Reden 2002.
 North 1990, and its application to the ancient world in Frier and Kehoe 2007; Bang 2009; the various papers in Manning and Morris 2005; Scheidel’s forthcoming Cambridge Companion to the Roman Economy
 Frier and Kehoe 2007.
 Frier and Kehoe 2007: 119.
 Bang 2006: 79.
 Bang 2006: 80-4.
 Bang 2008: 197.
 Lo Cascio 2006: 225.
 Lo Cascio 2006: 231.
 Graham 2006b: 111-113.
 cf. Davis’ diagrams in 2005; 1998.
 Brughmans 2012; 2010.
 Brughmans 2012; Bentley and Maschner 2003:1.
 Knappett 2011: 10.
 Schortman and Ashmore 2012: 3.
 Schortman and Ashmore 2012: 2-3 citing Barrett 2000, Bourdieu 1977, Dobres and Robb 2000; Dornan 2002; Garnder 2007; Giddens 1984.
 Schortman and Ashmore 2012: 3-4.
 Milgram 1967.
 Buchanan 2002; Watts and Strogatz 1998; Watts 1999.
 Brughmans 2010.
 Shortman and Ashmore 2012: 4.
 Graham and Ruffini 2007: 325-331.
 Coward 2010.
 Weingart 2012.
 Brughmans 2012; 2010.
 cf. Wilensky and Resnick 1998: 4; Agar 2003; Gilbert and Troitzsch 2005: 199-202. Gilbert and Troitzsch is an excellent resource for learning how to build simulations.
 Tesfatsion 2006: 840.
 Tesfatsion 2006:843.
 Watts and Strogatz 1998; Barabasi and Albert 1999.
 Janssen and Ostrom 2005: 1496 citing Dibble 2006; Wilhite 2006; Vriend 2006.
 Berger 2001.
 Deffuant et al., 2002.
 Hoffmann et al 2002; Evans and Kelley 2004.
 Some of the earliest work being in Clarke 1968, 1972; see Graham 2006a: 53-4.
 The premier journal is the Journal of Artificial Societies and Social Simulation, jasss.soc.surrey.ac.uk.
 Barrett 2001:155.
 for instance Lehner 2000; Kohler, Gumerman and Reynolds 2005; Dean et al 2006; Graham 2006a; Premo 2006; Graham and Steiner 2008; Symons and Raine 2008; Graham 2009; Costopoulos and Lake 2010.
 Tesfatsion 2006: 852-3.
 cf. Wallace-Hadrill 1989.
 Bang 2006; 2008
 Doran 2011.
 Wilensky 1999.
 Bogost, 2007: 28-44.
 Ourednik and Dessemontet 2007.
 Wilensky, 1998.
 Published in Graham 2009.
 Graham 2009: 11.
 cf. DeLaine 2002 on patronage in building projects at Ostia.
 Malkin 2011: 18-9.
I’ve been asked this morning to talk to our new cohort of MA students on ‘doing digital history’. I thought about how I might do this. Typically, I’d throw together a powerpoint and begin talking about various tools, trends in the field, try to get a sense of what people are interested in, tailoring my comments to those topics.
Today though, I’m trying a different approach. I want students to understand that the choice of tool, and the way data gets represented or manipulated in a computer, are not inconsequential choices. To that end, I’m working with Prezi. Its metaphor – zooming – couldn’t be further from Powerpoint – recreating the 35 mm slide. It’s a simple example, but I think it should make the point elegantly. Now, as to ‘getting started with digital tools’, I could’ve just sent the students to the DiRT wiki (which I am indeed doing), leaving it at that, but again, I wanted to make my larger point have more resonance. In which case, I’m going to take them through my digital workflow as I use topic modeling to try to understand deeper structures in the corpus of ancient writers. I’m not pretending to comprehensiveness this morning. Rather, I am using my own research (and how I came to this research) as a trajectory for launching students into their own research. Below is my prezi; please feel free to use, adapt, alter accordingly to your own needs.
I’ve been having an interesting conversation with Ben Marwick, in the comments thread of my initial ‘Getting Started with Topic Modeling’ post. Ben pointed me to an interesting GUI for Mallet, which may be downloaded here. I’ve been trying it out this morning, and I like what I’m seeing. Topic modeling is becoming more and more popular amongst the Digital Humanities crowd. An interesting automated approach to generating networks of topics and ideas from texts is reported by Scott Weingart, using the writings of Newton.
While I have nothing near so polished available, the GUI for Mallet used with Gephi can do nearly the same thing. My body of data comes from Writing History in the Digital Age. An earlier experiment with the same data is recounted here. I re-ran the data using the GUI approach, and have to say, this is a much easier and accessible approach. Run the program; select the folder with your txt documents in it; select the target number of topics; select the appropriate language stopwords list if necessary; hit ‘train topics’. What is very neat about this program is how it presents its output in both html and csv.
So in the spirit of crowdsourcing, I’ve put the output files online, and haven’t tried to decide yet what the topics might mean. Instead, why don’t you view the files for yourself, and let’s identify the topics using the comments of this post?
I then took the CSV files, and got them ready for import into Gephi. Decide which two columns you’d like to represent as being connected, and prune away the extraneous data. I took the ‘topicsindocs.csv’ file, and pruned it so that each paragraph of each author is paired with its major topic. I stripped away the info about the paragraph itself, so that the resulting visualization is just authors to the topics they write about. In the screenshot below, you can see the open gephi file with my own ‘Wikiblitz’ article highlighted, and its connections.
What’s also interesting is when I ran the ‘modularity’ routine – identifying communities based on patterns of self-similarity of ties – only four communities emerged (albeit with a very low modularity measurement, 0.235, which suggests that these communities are all that strong). A natural grouping of the papers, perhaps? (by the way, here’s the pdf/svg file).