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I want to develop an app that makes it difficult to move through the historically ‘thick’ places – think Zombie Run, but with a lot of noise when you are in a place that is historically dense with information. I want to ‘visualize’ history, but not bother with the usual ‘augmented reality’ malarky where we hold up a screen in front of our face. I want to hear the thickness, the discords, of history. I want to be arrested by the noise, and to stop still in my tracks, be forced to take my headphones off, and to really pay attention to my surroundings.
So here’s how that might work.
1. Find wikipedia articles about the place where you’re at. Happily, inkdroid.org has some code that does that, called ‘Ici’. Here’s the output from that for my office (on the Carleton campus):
2. I copied that page (so not the full wikipedia articles, just the opening bits displayed by Ici). Convert these wikipedia snippets into numbers. Let A=1, B=2, and so on. This site will do that:
3. Replace dashes with commas. Convert those numbers into music. Musical Algorithmns is your friend for that. I used the default settings, though I sped it up to 220 beats per minute. Listen for yourself here. There are a lot of wikipedia articles about the places around here; presumably if I did this on, say, my home village, the resulting music would be much less complex, sparse, quiet, slow. So if we increased the granularity, you’d start to get an acoustic soundscape of quiet/loud, pleasant/harsh sounds as you moved through space – a cost surface, a slope. Would it push you from the noisy areas to the quiet? Would you discover places you hadn’t known about? Would the quiet places begin to fill up as people discovered them?
Right now, each wikipedia article is played in succession. What I really need to do is feed the entirety of each article through the musical algorithm, and play them all at once. And I need a way to do all this automatically, and feed it to my smartphone. Maybe by building upon this tutorial from MIT’s App Inventor. Perhaps there’s someone out there who’d enjoy the challenge?
I mooted all this at the NCPH THATCamp last week – which prompted a great discussion about haptics, other ways of engaging the senses, for communicating public history. I hope to play at this over the summer, but it’s looking to be a very long summer of writing new courses, applying for tenure, y’know, stuff like that.
Edit April 26th – Stuart and I have been playing around with this idea this morning, and have been making some headway per his idea in the comments. Here’s a quick screengrab of it in action: http://www.screencast.com/t/DyN91yZ0
Below is a draft of the first part of my talk for Scholarslab this week, at the University of Virginia. It needs to be whittled down, but I thought that those of you who can’t drop by on Thursday might enjoy this sneak peak.
Thursday, March 21 at 2:00pm
in Scholars’ Lab, 4th floor Alderman Library.
When I go to parties, people will ask me, ‘what do you do?’. I’ll say, I’m in the history department at Carleton. If they don’t walk away, sometimes they’ll follow that up with, ‘I love history! I always wanted to be an archaeologist!’, to which I’ll say, ‘So did I!’
My background is in Roman archaeology. Somewhere along the line, I became a ‘digital humanist’, so I am honoured to be here to speak with you today, here at the epicentre, where the digital humanities movement all began.
If the digital humanities were a zombie flick, somewhere in this room would be patient zero.
Somewhere along the line, I became interested in the fossilized traces of social networks that I could find in the archaeology. I became deeply interested – I’m still interested – in exploring those networks with social network analysis. But I became disenchanted with the whole affair, because all I could develop were static snapshots of the networks at different times. I couldn’t fill in the gaps. Worse, I couldn’t really explore what flowed over those networks, or how those networks intersected with broader social & physical environments.
It was this problem that got me interested in agent based modeling. At the time, I had just won a postdoc in Roman Archaeology at the University of Manitoba with Lea Stirling. When pressed about what I was actually doing, I would glibly respond, ‘Oh, just a bit of practical necromancy, raising the dead, you know how it is’. Lea would just laugh, and once said to me, ‘I have no idea what it is you’re doing, but it seems cool, so let’s see what happens next!’
How amazing to meet someone with the confidence to dance out on a limb like that!
But there was truth in that glib response. It really is a form of practical necromancy, and the connections with actual necromancy and technologies of death is a bit more profound than I first considered.
So today, let me take you through a bit of the deep history of divination, necromancy, and talking with the dead; then we’ll consider modern simulation technologies as a form of divination in the same mold; and then I’ll discuss how we can use this power for good instead of evil, of how it fits into the oft-quote digital humanities ethos of ‘hacking as a way of knowing’ (which is rather like experimental archaeology, when you think about it), and how I’m able to generate a probabilistic historiography through this technique.
And like all good necromancers, it’s important to test things out on unwilling victims, so I would also like to thank the students of HIST3812 who’ve had all of the ideas road-tested on them earlier this term.
Zombies clearly fill a niche in modern western culture. The president of the University of Toronto recently spoke about ‘zombie ideas’ that despite our best efforts, persist, infect administrators, politicians, and students alike, trying to eat the brains of university education.
Zombies emerge in popular culture in times of angst, fear, and uncertainty. If hollywood has taught us anything, it’s that Zombies are bad news. Sometimes the zombies are formerly dead humans; sometimes they are humans who have been transformed. Sometimes we deliberately create a zombie. The zombie can be controlled, and made to do useful work; zombie as a kind of slavery. More often, the zombies break loose, or are the result of interfering with things humanity was wont not too; apocalypse beckons. But sometimes, like ‘Fido’, a zombie can be useful, can be harnessed, and somehow, be more human than the humans. [Fido]
If you’d like to raise the dead yourself, the answer is always just a click away [ehow].
There are other uses for the restless dead. Before our current fixation with apocalypse, the restless dead could be useful for keeping the world from ending.
In video games, we call this ‘the problem space’ – what is it that a particular simulation or interaction is trying to achieve? For humanity, at a cosmological level, the response to that problem is through necromancy and divination.
I’m generalizing horribly, of course, and the anthropologists in the audience are probably gritting their teeth. Nevertheless, when we look at the deep history and archaeology of many peoples, a lot can be tied to this problem of keeping the world from ending. A solution to the problem was to converse with those who had gone before, those who were currently inhabiting another realm. Shamanism was one such response. The agony of shamanism ties well into subsequent elaborations such as the ball games of mesoamerica, or other ‘game’ like experiences. The ritualized agony of the athlete was one portal into recreating the cosmogonies and cosmologies of a people, thus keeping the world going.
The bull-leaping game at Knossos is perhaps one example of this, according to some commentators. Some have seen in the plan of the middle minoan phase of this palace (towards the end of the 2nd millenium BC) a replication in architecture of a broader cosmology, that its very layout reflects the way the Minoans saw the world (this is partly also because this plan seems to replicate in other Minoan centres around the Aegean). Jeffrey Soles, pointing to the architectural play of light and shadow throughout the various levels of Knossos argues that this maze-like structure was all part of the ecstatic journey, and ties shamanism directly to the agonies of sport & game in this location. We don’t have the Minoans’ own stories, of course, but we do have these frescoes of bull-leaping, and other paraphernalia which tie in nicely with the later dark-age myths of Greece
So I’m making a connection here between the way a people see the world working, and their games & rituals. I’m arguing that the deep history of games is a simulation of how the world works.
This carries through to more recent periods as well. Herodotus wrote about the coming of the Etruscans to Italy: “In the reign of Atys son of Menes there was a great scarcity of food in all Lydia. For a while the Lydians bore this with patience; but soon, when the famine continued, they looked for remedies, and various plans were suggested. It was then that they invented the games of dice, knucklebones, and ball, and all the other games of pastime, except for checkers, which the Lydians do not claim to have invented. Then, using their discovery to forget all about the famine, they would play every other day, all day, so that they would not have to eat… This was their way of life for eighteen years. Since the famine still did not end, however, but grew worse, the king at last divided the people into two groups and made them draw lots, so that one should stay and the other leave the country’.
Here I think Herodotus misses the import of the games: not as a pasttime, but as a way of trying to control, predict, solve, or otherwise intercede with the divine, to resolve the famine. In later Etruscan and Roman society, gladiatorial games for instance were not about entertainment but rather about cleansing society of disruptive elements, about bringing everything into balance again, hence the elaborate theatre of death that developed.
The specialist never disappears though, the one who has that special connection with the other side and intercedes for broader society as it navigates that original problem space. These were the magicians and priests. But there is an important distinction here. The priest is passive in reading signs, portents, and omens. Religion is revealed, at its proper time and place, through proper observation of the rituals. The magician is active – he (and she) compels the numinous to reveal itself, the spirits are dragged into this realm; it is the magician’s skill and knowledge which causes the future to unfurl before her eye.
The priest was holy, the magician was unholy.
Straddling this divide is the Oracle. The oracle has both elements of revelation and compulsion. Any decent oracle worth its salt would not give a straight-up answer, either, but rather required layers of revelation and interpretation. At Delphi, the God spoke to the Pythia, the priestess, who sat on the stool over the crack in the earth. When the god spoke, the fumes from below would overcome her, causing her to babble and writhe uncontrollably. Priests would then ‘interpret’ the prophecy, in form of a riddle.
Why riddles? Riddles are ancient. They appear on cuneiform texts. Even Gollum knew what a true riddle should look like – a kind of lyric poem asking a question that guards the right answer in hints and wordplay.
‘I tremble at each breath of air/ And yet can heaviest burders bear. [implicit question being asked is who am I? – water]
We could not get away from a discussion of riddles in the digital humanities without of course mentioning the I-ching. It’s a collection of texts that, depending on dice throws, get combined and read in particular ways. Because this is essentially a number of yes-or-no answers, the book can be easily coded onto a computer or represented mechanically. In which case, it’s not really a ‘book’ at all, but a machine for producing riddles.
Ruth Wehlau writes, “Riddlers, like poets, imitate God by creating their own cosmos; they recreate through words, making familiar objects into something completely new, rearranging the parts of pieces of things to produce creatures with strange combinations of arms, legs, eyes and mouths. In this transformed world, a distorted mirror of the real world, the riddler is in control, but the reader has the ability to break the code and solve the mystery (wehlau 1997)
Riddles & divination are related, and are dangerous. But they also create a simulation, of how the world can come to be, of how it can be controlled.
One can almost see the impetus for necromancy, when living in a world described by riddles. Saul visits the Witch of Endor; Oddyseus goes straight to the source.
…and Professor Hix prefers the term ‘post mortem communications’. However you spin it, though, the element of compulsion, of speaking with the dead, marks it out as a transgression; necromancers and those who seek their aid never end well.
It remains true today, that those who practice simulation, are similarly held in dubious regard. If that was not the case, tongue in cheek articles titles such as this would not be necessary.
I am making the argument that modern computational simulation, especially in the humanities, is more akin to necromancy than it is to divination, for all of these reasons.
But it’s also the fact that we do our simulation through computation itself that marks this out as a kind of necromancy.
The history of the modern digital computer is tied up with the need to accurately simulate the yields of atomic bombs, of blast zones, and potential fallout, of death and war. Modern technoculture has its roots in the need to accurately model the outcome of nuclear war, an inversion of the age old problem space, ‘how can we keep the world from ending’ through the doctrines of mutually assured destruction.
The playfulness of those scientists, and the acceleration of hardware technology lead to video games, but that’s a talk for another day (and indeed, has been recently well treated by Rob MacDougall of Western University).
‘But wait! Are you implying that you can simulate humans just as you could individual bits of uranium and atoms, and so on, like the nuclear physicists?’ No, I’m not saying that, but it’s not for nothing that Isaac Asimov gave the world Hari Seldon & the idea of ‘psychohistory’ in the 1950s. As Wikipedia so ably puts it, “Psychohistory is a fictional science in Isaac Asimov’s Foundation universe which combines history, sociology, etc., and mathematical statistics to make general predictions about the future behavior of very large groups of people, such as the Galactic Empire.”
Even if you could do Seldon’s psychohistorical approach, it’s predicated on a population of an entire galaxy. One planetfull, or one empire-full, or one region-full, of people just isn’t enough. Remember, this is a talk on ‘practical’ necromancy, not science-fiction.
Well what about so-called ‘cliodynamics’? Cliodynamics looks for recurring patterns in aggregate statistics of human culture. It may well find such patterns, but it doesn’t really have anything to say about ‘why’ such patterns might emerge. Both psycohistory and cliodynamics are concerned with large aggregates of people. As an archaeologist, all I ever find are the traces of individuals, of individual decisions in the past. It always requires some sort of leap to jump from these individual traces to something larger like ‘the group’ or ‘the state’. A Roman aqueduct is, at base, still the result of many individual actions.
A practical necromancy therefore is a simulation of the individual.
There are many objections to simulation of human beings, rather than things like atoms, nuclear bombs, or the weather. Our simulations can only do what we program them to do. So they are only simulations of how we believe the world works (ah! Cosmology!). In some cases, like weather, our beliefs and reality match quite well, at least for a few days, and we know much about how the variables intersect. But, as complexity theory tells us, starting conditions strongly affect how things transpire. Therefore we forecast from multiple runs with slightly different starting conditions. That’s what a 10% chance of rain really means: We ran the simulation 100 times, and in 10 of them, rain emerged.
And humans are a whole lot more complex than the water cycle. In the case of humans, we don’t know all the variables; we don’t know how free will works; we don’t know how a given individual will react; we don’t understand how individuals and society influence each other. We do have theories though.
This isn’t a bug, it’s a feature. The direction of simulation is misplaced. We cannot really simulate the future, except in extremely circumscribed situations, such as pedestrian flow. So let us not simulate the future, as humanists. Let us create some zombies, and see how they interact. Let our zombies represent individuals in the past. Give these zombies rules for interacting that represent our best beliefs, our best stories, of how some aspect of the past worked. Let them interact. The resulting range of possible outcomes becomes a kind of probabilistic historiography. We end up with not just a story about the past, but also about other possible pasts that could have happened if our initial story we are telling about how individuals in the past acted is true, for a given value of true.
We create simulacra, zombies, empty husks representing past actors. We give them rules to be interpreted given local conditions. We set them in motion from various starting positions. We watch what emerges, and thus can sweep the entire behavior space, the entire realm of possible outcomes given this understanding. We map what did occur (as best as we understand it) against the predictions of the model. For the archaeologist, for the historian, the strength of agent based modeling is that it allows us to explore the unintended consequences inherent in the stories we tell about the past. This isn’t easy. But it can be done. And compared to actually raising the dead, it is indeed practical.
[and here begins part II, which runs through some of my published ABMS, what they do, why they do it. All of this has to fit within an hour, so I need to do some trimming.]
[Postscriptum, March 23: the image of the book of random digits came from Mark Sample's 'An Account of Randomness in Literary Computing, & was meant to remind me to talk about some of the things Mark brought up. As it happens, I didn't do that when I presented the other day, but you really should go read his post.]
I’m playing with p3d.in to host some three dimensional models I’ve been making with 123D Catch. These are models that I have been using in conjunction with Junaio to create augmented reality pop-up books (and other things; more on that anon). Putting these 3d objects onto a webpage (or heaven forbid, a pdf) has been strangely much more complicated and time-consuming. P3d.in then serves a very useful purpose then!
Below are two models that I made using 123D catch. The first is the end of a log recovered from anaerobic conditions at the bottom of the Ottawa River (which is very, very deep in places). The Ottawa was used as a conduit for floating timber from its enormous watershed to markets in the US and the UK for nearly two hundred years. Millions of logs floated down annually…. so there’s a lot of money sitting down there. A local company, Log’s End, has been recovering these old growth logs and turning them into high-end wide plank flooring. They can’t use the ends of the logs as they are usually quite damaged, so my father picked some up and gave them to me, knowing my interest in all things stamped. This one carries an S within a V, which dates it to the time and timber limits of J.R. Booth I believe.
And here we have one of the models that my students made last year from the Mesoamerican materials conserved at the Canadian Museum of Civilization (soon-to-be-repurposed as the Museum of Canadian History; what will happen to these awkward materials that no longer fit the new mandate?)
Incidentally, I’ve now embedded these in a Neatline exhibition I am building:
(originally posted at #HIST3812, my course blog for this term’s History3812: Gaming and Simulations for Historians, at Carleton University).
I play because I enjoy video games, obviously, but I also get something else out of it. Games are a ‘lively art’; they are an expressive art, and the artistry lies in encoding rules (descriptions) about how the world works at some microlevel: and then watching how this artistry is further expressed in the unintended consequences of those rules, their intersections, their cancellations, causing new phenomena to emerge.
This strikes me as the most profound use of humanities computation out there. Physicists tell us that the world is made of itty bitty things that interact in particular ways. In which case, everything else is emergent: including history. I’m not saying that there are ‘laws’ of human action; but we do live in this universe. So, if I can understand some small part of the way life was lived in the past, I can model that understanding, and explore the unintended outcomes of that understanding… and go back to the beginning and model those.
I grew up with the video game industry. Adventure? I played that. We had a vic-20 . If you wanted to play a game, you had to type it in yourself. There used to be a magaine (Compute!) that would have all of the code printed within, along with screenshots. Snake, Tank Wars – yep. My older brother would type, and I would read the individual letters (and spaces, and characters) out. After about a week, we’d have a game.
And there would be bugs. O lord, there were bugs.
When we could afford games, we’d buy text adventures from Infocom. In high school, my older brother programmed a quiz game as his history project for the year. Gosh, we were cool. But it was! Here we were, making the machine do things.
As the years went on, I stopped programming my own games. Graphics & technology had moved too fast. In college, we used to play Doom (in a darkened room, with the computer wired to the stereo. Beer often figured). We played SimCity. We played the original Civilization.
These are the games that framed my interactions with computers. Then, after I finished my PhD, I returned to programming when I realized that I could use the incredible artificial intelligences, the simulation engines, of modern games, to do research. To enhance my teaching.
I got into Agent Based Modeling, using the Netlogo platform. This turned my career around: I ceased to be a run-of-the-mill materials specialist (Roman archaeology), and became this new thing, a ‘digital humanist’. Turns out, I’m now an expert on simulation and history.
And it’s all down to the fact that I’m a crappy player of games. I get more out of opening the hood, looking at how the thing works. Civilization IV and V are incredible simulation engines. So: what kinds of history are appropriate to simulate? What kinds of questions can we ask? That’s what I’m looking forward to exploring with you (and of course, seeing what you come up with in your final projects).
But maybe a more fruitful question to start with, in the context of the final project of this course, is, ‘what is the strangest game you’ve ever played?’
What made it strange? Was it the content, the mechanics, the interface?
I played one once where you had to draw the platform with crayons, and then the physics engine would take over. The point was to try to get a ball to roll up to a star. Draw a teeter-totter under the star, and perhaps the ball would fall on it, shooting the star up to fall down on the ball, for instance. A neat way of interacting with the underlying physics of game engines.
I’d encourage everyone to think differently about what the games might be. For instance, I could imagine a game that shows real-time documents (grabbed from a database), and you have to dive into it, following the connected discourses (procedurally generated using topic models and network graphing software to find these – and if this makes no sense to you, take a quick peek at the Programming Historian) within it to free the voices trapped within…
This is why I play. Because it makes me think differently about the materials I encounter.
Tomorrow in my HIST3812 I want to get students thinking about the kinds of history that might be appropriate to embody in a game or simulation, and the experience of such games. Inspired by something we did at THATCamp Great Lakes, I’ve taken a deck of cards and divided it into ‘historiography (hearts)’, ‘genre (spades)’, and ‘aesthetic (clubs)’. Here’s the prompt for the exercise:
“I will give you cards from three different decks:
- historiography (Hearts)
- genre (Spades)
- aesthetic (Clubs)
Look at your cards. In your groups, brainstorm a quick idea for a game using those cards. If, after five minutes, you’ve hit a blank, you may exchange one card, and one card only. Note that nothing is being said about mechanics…
(what you come up with today is not necessarily what you have to go with for the term. This is just meant to get you thinking.)
|Historiography (Hearts)||Genre (Spades)||Aesthetics (Clubs)|
|1 – Comparative||1 – ARG||1 or A – sensation|
|2 – Cultural||2 – Platformer||2 or K – fantasy|
|3 – Oral||3 – Shooter||3 or Q – narrative|
|4 – Economic||4 – Action-adventure||4 or J – challenge|
|5 – Environmental||5 or 10 – Adventure||5 or 10 – fellowship|
|6 – World||6 or J – RPG||6 or 9 – discovery|
|7 – Family||7 or Q – Simulation||7 – submission|
|8 – Gender||8 or K – Strategy||8 – expression|
|9 – Religious||9 – Casual|
|10 – Intellectual||A – Serious|
|J – Labour|
|Q – Marxist|
|K – Microhistory|
|A – Public|
I fed two recent posts, ‘Evaluating Digital Humanities‘, and ‘Deformative Digital Archaeology’, into Textexture.com. Textexture topic models your input texts, and then visualizes them via Gephi so that you can explore the interlinkages of topics/discourses whilst revisualizing them at the same time. You can play and explore the results for yourself at:
http://textexture.com/index.php?text_id=6941 Evaluating Digital Humanities
http://textexture.com/index.php?text_id=6943 Deformative Digital Archaeology.
You’ll want to hit ‘start layout’ to make these look a bit more presentable. Note that you can also download the gexf file itself, to open in Gephi, to try other layouts/metrics.
I find it reassuring, somehow, that natural divisions in my texts (for instance, in the second image, the code explications are clearly distinct, in red, from the broader discussion on the nature of digital archaeology, in blue). Unfortunately, Textexture only deals with relatively smallish chunks of text for now.
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.
I want to explore alternate ways of ‘visualizing’ patterns in data, beyond the visual. To that end, I’ve taken the major topics & their proportions from a topic model generated with MALLET and run them through the Musical Algortihms site at EWU.
1. I obtained data from the Portable Antiquities Scheme related to ceramic building materials recovered by the scheme (why this and not something else? I’m thinking about brick these days. No other reason).
2. I created a topic model of the descriptor text.
3. I take the composition file that is outputed (the one that can be read as ‘in document 2 the major topic is 4 at 25%, then topic 6 at 12%…’ etc), and grab the topics and the amount by which they compose the document- so the first two columns. I turn the decimals into whole numbers by multiplying by 100.
4. I put these two columns into Musical Algorithmns. I perform the modulo scaling, then I invert the numbers. I used a 1 for the duration of the note.
You can listen to the output here
So what does it sound like? Well, I haven’t got there yet. But… if you do the whole process again, this time with topic models derived from writing qua writing (rather than database entries; the link takes you to topic models I did from posts on Play the Past), you get this. Which sounds markedly different. More structure. Less repetition.
Anyway, this is obviously something that’ll require some more playing around (ha – see what I did there?)
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