Week Three Blogpost

The readings for this week highlight three limitations for computational algorithms:

  1. Logical Contradiction
  2. Private Ownership of Data
  3. Mimetic Fidelity

In “What is Computable?,” MacCormick uses deductive reasoning to prove certain types of computer programs logically contradictory, particularly the creation of a computer program that identifies whether or not other programs will crash. Except for a brief mention of phenomenology and spirituality at the end, he focuses almost exclusively on the logical limits of algorithms, insisting that everything not logically contradictory is at least theoretically computable.

However, Kugler’s article, “What Happens When Big Data Blunders?” (interestingly both articles are phrased as questions), uncovers two other issues through case studies: Google’s attempt to predict flu trends and the WHO/CDC attempts to predict ebola trends. These two big data projects failed, not from logical contradictions, but from commercial bias and mimetic infidelity respectively. In the first case, Google Flu Trends were based on a commercial search algorithm that changes based on fluctuating business plans. This presents difficulties beyond the comparatively clear-cut deductive reasoning of MacCormick, questioning whether a commercial venture determined by profit and competition can provide reliable algorithms for scientific research. While perhaps practically difficult, there’s no theoretical reason why such issues cannot be resolved through, say, performing such research outside of the commercial sphere.

In contrast, the WHO/CDC case study uncovers a much more difficult (and perhaps unanswerable) question: what are the limits of computational simulation? The WHO CDC studies used simulations that extrapolated from initial conditions to approximate ebola deaths, failing to keep up with “the ever-changing situation on the ground”–what we might call “reality.” This opens up philosophical questions going back to Plato regarding the relation between representation and reality. Further, in the age of computer simulation, what are the conditions necessary to render reality representable in a computational environment? Does reality itself have to function according to the principles of computation?  

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