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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.
Archaeological Glitch Art, Bill Caraher


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.
Screen shot from the deformed Netlogo ‘Mimicry’ model


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.

 

 

 
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Most influential keywords in this text:
model    type    amphora    digital    filter: off

Most influential contexts in this text:
#0:   model    monarch    viceroy    swap    filter: off
#1:   type    amphora    color    greek    filter: off
#2:   digital    similar    archaeological    data    filter: off
#3:   code    kind    human    change    filter: off

 

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