Weights + Measures: Probing a System of Relative Values

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Deepthi Welaratna

Mediated Environments

May 6, 2010

Weights + Measures: Probing a System of Relative Values

Consider the elephant. Largest of the land mammals, gentle herbivores
by nature but forced into war and slavery by history. Sometimes comical
in it
s improbable proportions and ungainly gait, the elephant becomes
remarkably agile in water. Now consider the airplane. Lighter than air
yet earth
bound. Emblematic of an age of war and the promise of
technological supremacy, but the embodiment of the trage
dy of human
failings when underwater. And finally, in the ocean, drifting alongside
the graceful elephant and sinking airplane are the many plankton,
navigating that complex liquid through a delicate inaction. Mostly
invisible in size and scope, yet indisp
ensable to the health of the
planet and all life in it. One of the most basic life
forms on earth
but also the most plentiful and occasionally dazzling to behold.

As Marina Zurkow’s linear animation
Weights + Measures

the relationships betw
een these three beautiful “machines” drift in and
out of focus when suspended in the system of the ocean. Airplanes sink
while elephants swim. Elephants and airplanes both release methane, and
both have been instruments of transport and war. Airplanes prod
carbon dioxide (CO2), while plankton consumes it. As the largest land
mammal, elephants are at the top of the terrestrial food chain, and
microscopic plankton is at the bottom; yet without phytoplankton, the
oceans would starve. Each machine brings int
o focus several facets of a
complex ecosystem, which includes the economics of short
imperatives and long
view evolutionary time; the microscopic and the
monumental; and human interventions of biological technology.


But a linear animation can only g
o so far in probing the relationships
of relative values in an age of visual complexity. Computing, with its
basic building blocks of data structures and algorithms, offers
seemingly endless ways to re
present and revisualize these ideas,
introducing incre
asing layers of depth and detail to ever
greater end.
As Roger F. Malina writes in “Digital Image

Digital Cinema: The Work of
Art in the Age of Post
Mechanical Reproduction,” “the unique computer
tools available to the artist, such those of image processin
visualization, simulation and network communication are tools for
changing, moving
, and
, not for

digital information”
(33). This paper lays out how
Weights +Measures

has the potential to
evolve into a data
driven exploration of relat
ive measures, drawing on
database aesthetics, the capacity for emergence in visual systems, and
the unique properties of multi
stream visual storytelling as
exemplified in two data
driven art projects:

We Feel Fine

Database Aesthetics

As Le
v Manovich states in his seminal essay “Database as Symbolic
Form,” computing has introduced a new ontology of the world through the
dynamic duo of data structures and algorithms (43). Let’s start with
data structures, which I’ll be using somewhat intercha
ngeably with
databases. As Christiane Paul defines in “The Database as System and
Cultural Form: Anatomies of Cultural Narratives,” a database is “a
structured collection of data that stands in the tradition of ‘data
containers’ such as a book, a library,
an archive, or Wunderkammer”
(95). The data container names and shapes the information, and the
structures are designed (written) to be used by algorithms for
different purposes.


But before we move on to the algorithm, it’s important to interrogate
the f
orm and meaning of the database to understand how it is culturally
shaped rather than simply presenting an objective list of facts. (In
fact, using the very term “list” already imposes order and
circumscribes the universe of data contained within.) Lev Man
ovich gets
us started. He states, “As a cultural form, database represents the
world as a list of items, and it refuses to order this list” (44). But
the lack of ordering Manovich describes does not negate an essentially
cultural form of ordering that must

take place to create the data
structure, through the naming of the data and shaping of the data

Manovich argues that databases are essentially different from
narratives, because of this lack of order, stating that “a narrative
creates a cause
effect trajectory of seemingly unordered items
(events). Therefore, database and narrative are natural enemies.
Competing for the same territory of human culture, each claims an
exclusive right to make meaning out of the world” (44). But a database

not the blank slate of a world of objects, as Manovich seems to
argue. There is already a naming process that has imposed meaning and
relationships, although it's usually tempting for computer scientists
and others who work with information technologies t
o overlook this
biased act of meaning

The definition of the containers and the naming act are the very first
making activities based on narrative logic. As Paul writes,
"every 'container' of information ultimately constitutes a

and architecture of its own" (95). I would thus argue that narrative

and database aesthetics, rather than being “natural enemies,” can never
be mutually exclusive. This is the only way that databases and archives
can “serve as ready
made comment
aries on our contemporary social and
political lives” as Victoria Vesna argues in the introduction to
Database Aesthetics: Art in the Age of Information Overflow

(xi). If
there wasn’t a basic imposition of some kind of cultural logic on the
data, databases

couldn’t be used by artists as “a vehicle for
commenting on cultural and institutional practices” (Vesna xi).

Now that we’ve established that the database is at its core a
culturally meaningful form, let’s move to the algorithm to understand
its role in
creating new forms of representation. While the algorithm
is typically described in engineering sciences as a way to solve a
problem, in the world of databases, the algorithm is the primary
element that organizes and manipulates the information to some end

display. Through processing, algorithms can combine different kinds of
data, perform operations on them, and bring them into a system to see
how they interact with each other (it is particularly this last
application that we will return to in the project
description). Endless
numbers of algorithms can be written to draw on the same primary
database, which is why "database aesthetics often becomes a conceptual
potential and cultural form

a way of revealing (visual) patterns of
knowledge, beliefs, and social

behavior" (Paul 95). This brings us to
our next arena of inquiry, emergence in visual systems.

Emergence in Visual Systems


Matt Rohrer writes that “of all our brain functions, our vision system
has the highest capacity for processing information” (24)
. In a simple
example, visualizations are able to take lists of numbers and represent
them as ascending or descending lines, moving the mental work for the
viewer from processing the number trends to seeing the interpretation.
As visual modeling becomes mo
re complex, the relationships represented
between datasets correspondingly become more interpretive, freeing up
the mind to move on to more sophisticated analyses.

By providing alternate ways of accessing and displaying the information
contained in a dat
abase, visualizations can impact the audience's
comprehension of the meaning of the data. And by creating a
metaphorically charged vehicle for the data, visual systems can spur
unexpected relationships in the viewer's mind, as John Klima
accomplishes in th
e data
driven art project
, a project that
prefigures the potential of
Weights + Measures

to a degree.

Visualizations are often perceived as simulations, or ways to represent
as aspect of the natural world or a process fairly faithfully. But as
erry Turkle explores in
Simulation and its Discontents
, simulations
often limit original thinking, introduce biases and distortions, and
perhaps worst, risk confusing virtual realities with the real. Rather
than fall into this final trap, it’s important to

link visualization
with the notion of emergence to see that in simulations, the real
strength is the ability to stimulate new ways of understanding the same

Emergence is today vital to understanding the value of visual systems,
but can be a chall
enge to define with any consistency. The end result

of emergence is to create a dynamic enough system that when it reaches
a certain degree of complexity in its organization, it will begin to
exhibit genuinely new properties. This ability to transcend the
of each of the individual elements in the system in a greater whole is
what makes emergence so exciting. Jaegwon Kim writes in “Emergence:
Core Ideas and Issues,” that the concept has gone through countless
iterations and revisions while becoming in
creasingly popular in systems
theory. He states that “the intuitive associations this word evokes in
us do not add up to a concept robust enough to do any useful work” and
goes on to question the viability of emergence based on existing

But for t
he purposes of
Weights + Measures
, emergent properties are
possible to evoke, if not the exact reality of emergence, through the
application of open data streams to a closed environment and the
juxtaposition of different types of information. The goal, rat
her than
being to draw concrete conclusions or deliver a specific statement, is
to generate a recombinant imaginative space which foregrounds
relationships, pulling focus from the data to the difference.

stream Visual Storytelling

Manovich first in
troduced the notion of database stories, which he sees
as the replacement for linear stories. He says that these multilinear
stories, which Paul also characterizes as “an interactive narrative or
hypernarrative” are “the sum of multiple trajectories throug
h a
database” (Paul 101,

Manovich 46). There are already examples of art
projects that embody these multilinear stories to create a
hypernarrative, of which I will offer two examples here.


Weights + Measures

perhaps most closely follows in the footsteps
John Klima's
, which represents the global financial markets as
birds and trees. The actions of each flock of birds are driven by a
distinct global currency value. Klima writes that “there is something
directly evocative, appropriate, and meanin
gful in the use of global
currency values, where one country’s gain is another’s demise.” In
Klima's system, he creates states for each the currencies that dictate
behaviors on the part of the flocks

in one volatile state,
"aggressing," the daily volatilit
y of a global currency is at least
three times its yearly volatility. This is a meaningful state for
traders and is represented in the flock through attacks on other

Klima designed the system to be emergent, writing that he “had the
sense that ba
sed on [the] rules and how I implemented them, something
interesting would naturally emerge” (264). After running the program
for a few days, Klima “was stunned to discover that the Middle Eastern
region was a swarm of aggression

flocks constantly attackin
g other
flocks” (264). This isn’t necessarily perfectly emergent in that a
sustained study of the data streams driving the project would perhaps
result in the same conclusions. But making a connection between the
symbolic forms in the ecosystem and the dat
a streams driving them
approaches emergence. The metaphorical overlay of a different natural
system of birds and trees upon the financial data stimulates a
different type of analysis on the part of the viewer.

In another web
based data
driven art project
We Feel Fine
, human
feelings are "harvested" from a large number of weblogs and displayed
in a "self
organizing particle system, where each particle represents a

single feeling posted by a single individual.” The particles have a
number of properties inc
luding color, size, shape, and opacity that
change based on the nature of the feeling being represented. Clicking
on a particle will display the feeling data shaping it. The vast
quantity of data creates a system in which “the particles careen wildly
d the screen until asked to self
organize along any number of
axes, expressing various pictures of human emotion."

We Feel Fine

is an odd aggregation of feeling data. While traditionally
quantitative data is seen as representational, qualitative data is
ften based on feelings. In quantitative systems, the greater the
volume of data, the greater the reliability of its representational
qualities. Quantitative data demands a closed system and invariably
results in a limited scope of conclusions that map dire
ctly onto
existing paradigms. This is how
We Feel Fine

approaches their vast
storehouse of emotions, presenting limited conclusions. When asked to
describe some of the more interesting conclusion, the project creators
write, “There's a bunch. For example,
on election day, there was a
spiking in the feeling ‘proud’ and ‘excited’. On Valentine's day,
people feel ‘loved’ and ‘lonely’ more than on other days. As people get
older, they tend to express less anger and disgust.” It is perhaps
regrettable that thes
e are the most interesting of the conclusions that
can be drawn, but it represents the limitations of quantitative

Qualitative data analysis, on the other hand, tends to take a different
approach, one driven by storytelling, and emotion
based a
ttempts to
derive insights from the human brain operating on context and small
slivers of data. Qualitative data is derived from a much smaller source

that does not attempt to make definitive statements about large groups
of people but instead uncovers the

unconscious drivers of behaviors, or
envisions existing truths in a new way

which sounds much more like

In the case of
Weights + Measures
, the plan is to create a system that
juxtaposes the economics of short
term imperatives and long
evolutionary time. We want to tell a story of unexpected outcomes and
unpredictable behaviors. There is potential to apply the metaphorical
approach of

that reclothes data streams in meaningful imagery
of birds and tress, just as
Weights + Measure

focuses on elephants,
airplanes and plankton. There is also the possibility to use a media
feed from a real
time source like Twitter that offers a meta commentary
on the data streams

similar to
We Feel Fine’s

feeling data but
without attempting to agg
regate the feelings into a representational

A possible limitation of the project is that we will be working with
already existing datasets in which the meaning and relationships are
already circumscribed to what's available and normative in the fie
through the data containers used to shape and break down the
information. But Paul writes that databases “lend themselves to a
categorization of information and narrative that can then be filtered
to create meta
narratives about the construction and cu
ltural specifics
of the original material” (101). By juxtaposing media feeds such as
popular Google search phrases or relevant YouTube video titles,
+ Measures

would attempt to add another layer of visual complexity that
uncovers relationships betw
een different kinds of data.
Weights +
, questioning as it does the mechanics of evaluation and

metrics, has the potential to be a meta
commentary on the very notion
of data as well as a complex visual system with emergent properties.


Works Cited

Harris, Jonathan, and Sep Kamvar.
We Feel Fine
. Aug. 2005. Web. Apr.
May 2010.

Kim, Jaegwon. "Emergence: Core Ideas and Issues."

Perspectives on Reduction and Emergence in Physics 151.3 (2006):
. Web. 29 Apr. 2010.

Malina, Roger
F. "Digital Image

Digital Cinema: The Work of Art in the
Age of Post
Mechanical Reproduction."

3 (1990): 33
. Web. 29 Apr. 2010.

Manovich, Lev. “Database as Symbolic Form.” Vesna 39
54. Print.

Paul, Christiane. “The Database as System an
d Cultural Form: Anatomies
of Cultural Narratives.” Vesna 95
109. Print.

Rohrer, Matt. "Seeing Is Believing: the Importance of Visualization in
Manufacturing Simulation."
Proceedings of the 32nd Conference on
Winter Simulation

(2000): 24
EBSCOhost Aca
demic Search
. Web. 29 Apr. 2010.

Turkle, Sherry. "What Does Simulation Want?"
Simulation and Its
. Cambridge, Mass.: MIT, 2009. 3
8. Print.

Vesna, Victoria. “Introduction.” Vesna x
xiii. Print.

Vesna, Victoria, ed.
Database Aesthetics: A
rt in the Age of Information
. Minneapolis: Minn., 2007. Print.