Excerpt from “Adrift with a Draft: Graphic Design & the Poetic Potential of Technology” (RISD Graduate Thesis)

On April 23, 2013, just after 1 pm, in a matter of seconds $121 billion of value was wiped from the S&P 500. The crash came seemingly from nowhere, and sent traders scrambling for a reasonsome piece of bad economic news, a terrorist attack, a natural disaster. Days of investigation turned up a curious cause: a hoax tweet suggesting that President Obama had been attacked at the White House.

But it wasn’t the emotional overreaction of traders that caused the crash. Instead, it was an army of algorithms, trained to comb the news for events that could impact the markets. Thousands of automated trading platforms seized on the story and set off a chain reaction within this intricately and mysteriously interconnected network.

This jarring event is just one example of an all-too-common phenomenon: we’ve created vast, intricate systems that govern the world around us but are too complicated for us to understand. While each of these trading algorithms was carefully and individually programmed by a person, taken together, their interactions are too vast and too unpredictable for us to make sense of.

I’ve had the privilege of being a member of the last generation to remember a time before the internet became a ubiquitous part of our lives, and though it seems distant and dreamlike now, I do remember an adolescence spent offline.

While the internet has triggered inconceivable advances in the ability to generate, deliver, organize and provide access to more information than ever before, it’s also made it more difficult to access that information on a personal level. We are creating content and adding knowledge at a pace that far outstrips our ability to make sense of it, and the result is systems and complexity of content that we don’tand can’tunderstand.

So how do we approach systems that operate on an inconceivable scale? Writer and Harvard Fellow Samuel Arbesman uses weather as an analogy. Even armed with satellites, radar and advanced computer modeling, we don’t fully understand weather systems. There are simply too many inputs, too many unknowns. Yet we develop extremely simplified models, and using just a few, isolated variables, we develop reasonably reliable forecasts. We don’t expect perfect understanding or perfect accuracywhen the forecast is wrong, we just reach for an umbrella and deal with it. We understand that there are gaps in our knowledge, and that the model often falls far short of perfection, but it allows us to achieve a general understanding of this vastly complex system, and learn to live within it.

I think there’s an opportunity to open up a similar line of thinking in designto abandon attempts at precise understanding or perfect explanation, and instead, to open up access points. To take something complex or fundamentally unknowable, and to make it accessible on a human level. Like our approach to weather, this involves isolating single variables and controlling the rest; reduction to a reproducible scale; and simplification for the purposes of understanding.

Perhaps it’s time to take a step back from the creation of new content, and instead to use what we already have. To filter the stream through the simple acts of collecting, selecting and sorting. To insert little obstructions in the overwhelming flow, and to observe and reflect on the content that pools up around them.

Rather than attempt to explain vast, complex systems in a comprehensive or precise way, this method allows understanding by metaphor or analogyuniquely human ways of understanding. Unlike the rigid databases or optimization structures that allow Google’s search algorithm to retrieve the most precise results, perhaps we can build systems that interact with content in ways that invite uncertainty, chance and serendipity. Systems that are reactive to human thought, with output that only make sense because we are people. As Lev Manovich writes in a recent article, designers “should not forget that art has the unique license to portray human subjectivityincluding this fundamental new dimension of being ‘immersed in data.’”

This is Where, a publication project of mine, explores this idea by pairing random Google Streetview images with random tweets that contain the phrase “this is where.” To a computer, the process is entirely random, meaningless noise. But to the human reader, the pairings take on a narrative form that is often epic in scope. The tweetsmostly clichés or minor observations in the context of a Twitter feedbecome momentous statements of purpose, recollections of love or loss, when placed against the backdrop of a sprawling vista of rolling hills and wide-open sky.

The effect is achieved because of a flaw in human thinkingan insistence on reading narrative structure or contextual relationship even where we know there is nonebut the very act of reading This is Where lets us engage with endless streams of content in a way that feels essentially personal.

This approach, I think, exists at a right angle to the historical practice of graphic design, which has often privileged rigorous organization, perfect grids and explanatory clarity. Here, my focus is on small gestures, question asking and thought experiments. And my approach is strongly informed by a practice introduced by 1960s Conceptual Art and followed by Appropriation Artof searching, archiving and reusing the existing glut of visual and textual material.

It’s a subtle shift: trading understanding for open inquiry, instantaneity for uncertainty, pushing forward for lateral reflection. Perhaps we can flatly admit that we’ll never really understand the world around us. This admission, I think, frees us not just to see new things, but to look in different ways, with different goals and expectations.

Works cited

“Kevin Slavin: How algorithms shape our world.” TED.com. http://www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world

“It’s complicated.” Aeon Magazine. http://aeon.co/magazine/world-views/is-technology-making-the-world-too-complex/ 

“The Anti-Sublime Ideal in Data Art.” Lev Manovich. http://www.manovich.net/data_art.doc