In Humanities Computing, Willard McCarty notes that “computational form, which accepts only that which can be told with programmatic explicitness and precission, is thus radically inadequate for representing the full range of knowledge – hence useful for isolating what gets lost when we try to specify the unspecifiable.” (25).  In other words, there are certain ways of knowing that we cannot explain, but because computers can only accept concise directions, they allow us to understand what’s missing when we do try to model these ways of knowing. To attempt to explicate something human through a series of instructions, you can compare the result to what you feel is the the result, and adapt the instructions as necessary. Thus, as Willard McCarty did in his research on personification in Ovid’s Metamorphoses, the process of modelling becomes an iterative process of comparing, identifying, and changing. However, the trick to improvement is that  any changes affect all examples, and thus a change to accommodate a misnomer must also not break the model’s tolerance for something already accounted for. Or, if it does break it, perhaps it had not been explained by the model in the first place.

Such a process of modelling is apparent in what’s perhaps the most well known datamining project: the Netflix Prize. The Netflix Prize is a $1 million prize being offered by Netflix to the team that can can improve their recommendation system algorithm by a baseline of 10%. The contest has been running since October 2006 and teams are in the home stretch. Eleven teams are at 9% or higher, with the top two teams at 9.64% and 9.63%. However, progress has slowed to a crawl, as the teams push the limits of how much a computer can understand the intricacies of human preference.

Throughout the contest, teams have been remarkably open about their strategy, “acting more like academics huddled over a knotty problem than entrepreneurs jostling for a $1 million payday” (Wired – This Psychologist Might Outsmart the Math Brains Competing for the Netflix Prize). Thus, we see the effects of the iterative process of modelling: one team has an “a-ha” moment, notes the idea to the community and suddenly, everyone else has the same eureka moment.

What I find most fascinating about the prize is that there is a limit to what can be done. It apparently took only a month, out of the last two and a half years, for the leaders to get halfway there. Yet, now everyone’s poring over those misnomers, and can’t quite figure out why people like the most polarizing of films. The New York Times Magazine refers to this as the “Napoleon Dynamite” problem, after one of the worst of the misnomers. Other ones include “I Heart Huckabees,” “Lost in Translation,” “Fahrenheit 9/11,” “The Life Aquatic With Steve Zissou,” “Kill Bill: Volume 1” and “Sideways”.