Long Form

Weights + Measures: 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 its 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 tragedy 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 indispensable 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 demonstrates, the relationships between 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 produce 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 into focus several facets of a complex ecosystem, which includes the economics of short-term imperatives and long-view evolutionary time; the microscopic and the monumental; and human interventions of biological technology.

But a linear animation can only go 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 increasing 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 processing, visualization, simulation and network communication are tools for changing, moving, and transforming, not for fixing digital information” (33). This paper lays out how Weights +Measures has the potential to evolve into a data-driven exploration of relative 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: ecosystm and We Feel Fine.

Database Aesthetics As Lev 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 interchangeably 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 form 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 Manovich 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 container.

Manovich argues that databases are essentially different from narratives, because of this lack of order, stating that “a narrative creates a cause-and-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 is 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 to overlook this culturally-biased act of meaning-making.

The definition of the containers and the naming act are the very first meaning-making activities based on narrative logic. As Paul writes, "every 'container' of information ultimately constitutes a dataspace 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 commentaries 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 more 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 database, 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 the data-driven art project ecosystm, 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 Sherry 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 thing.

Emergence is today vital to understanding the value of visual systems, but can be a challenge 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 impact 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 increasingly 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 logics.

But for the 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, rather 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.

Multi-stream Visual Storytelling Manovich first introduced 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 through 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 of John Klima's ecosystm, 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 meaningful 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 volatility 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 flocks.

Klima designed the system to be emergent, writing that he “had the sense that based 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 attacking 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 data 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 including 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 around 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 often 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 directly 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 these are the most interesting of the conclusions that can be drawn, but it represents the limitations of quantitative analysis.

Qualitative data analysis, on the other hand, tends to take a different approach, one driven by storytelling, and emotion-based attempts 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 re-envisions existing truths in a new way—which sounds much more like emergence.

In the case of Weights + Measures, the plan is to create a system that juxtaposes the economics of short-term imperatives and long-view evolutionary time. We want to tell a story of unexpected outcomes and unpredictable behaviors. There is potential to apply the metaphorical approach of ecosystm that reclothes data streams in meaningful imagery of birds and tress, just as Weights + Measures 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 aggregate the feelings into a representational form.

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 fields 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 cultural specifics of the original material” (101). By juxtaposing media feeds such as popular Google search phrases or relevant YouTube video titles, Weights + Measures would attempt to add another layer of visual complexity that uncovers relationships between different kinds of data. Weights + Measures, 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." Synthese New Perspectives on Reduction and Emergence in Physics 151.3 (2006): 547-59. JSTOR. Web. 29 Apr. 2010. Malina, Roger F. "Digital Image—Digital Cinema: The Work of Art in the Age of Post-Mechanical Reproduction." Leonardo 3 (1990): 33-38. JSTOR. Web. 29 Apr. 2010. Manovich, Lev. “Database as Symbolic Form.” Vesna 39-54. Print. Paul, Christiane. “The Database as System and 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): 24-27. EBSCOhost Academic Search Premier. Web. 29 Apr. 2010. Turkle, Sherry. "What Does Simulation Want?" Simulation and Its Discontents. Cambridge, Mass.: MIT, 2009. 3-8. Print. Vesna, Victoria. “Introduction.” Vesna x-xiii. Print. Vesna, Victoria, ed. . Minneapolis: Minn., 2007. Print.