This is the moment I have been waiting for so desperately: I am currently formatting my new computer’s hard drive—all by myself! However, it does not really make me feel like the mature nerd I always wanted to be, rather like the exile Swabian lacking her brother’s assistance. The best news is certainly that there is a new computer to work on—finally. I have been waiting for this sustainably produced (or green washed?!) HP device for two months, checking in at the store several times, in order to ask if it has arrived yet. The answer was always negative. They had problems with the delivery, suggested to lend me another computer, or to sell an alternative device for the same price. (Why would I borrow a laptop, format it, encrypt the hard drive, and return it after a few weeks? I did not lay out my reasons. I politely declined the offer.) When I came in today to cancel my order, however, without my wallet, only with $ 10 in my back pocket, having had lunch with two fellow grad students, the guy at the check-out went to the stacks and returned with my “EliteBook.” Everybody seemed seriously embarrassed for forgetting to notify me of the computer’s arrival, and they gave me a $ 100 discount. Nice. I could not take the computer with me, though, since I did not have a card for the refund on me. Less nice.
While I am installing Microsoft Office, I want to share some premature thoughts on mathematics and information technology as tools in the generation of knowledge. In one of our recent Monday Workshops, the Department had the pleasure of listening to and discussing Mary Morgan‘s presentation on the history and philosophy of economic models. Her account of “Economic Models: Fictions or Artefacts?”1 was based on the understanding that models do not represent the world itself, but are rather descriptions of our imagining of it. To determine if these descriptions are good or bad, she argued, we need “keys,” tools to translate parts of the model in real-world applications. Penetrated to describe in one noun what models do, she resistantly chose: “experimenting with artefacts.”
This is where she got me. If models are “experimenting,” there has to be some novel outcome of using models. What I am usually holding against astrophysics and their claim to be an experimental science, is that there is no refractory matter involved in their experiment, no entity to resist their attempts of acquiring anticipated results. How can we learn anything from models, if we design the “maps” for using the “keys” ourselves, according to our assumptions, and in line with already existing systems of knowledge? Mary Morgan asked a related, and yet very different question: Why would you model stuff, if you knew what it was and how it worked?
I want to suggest a possible answer: To create a narrative for the already (yet, unconsciously) known, to illustrate and clarify already existing vague beliefs, parts of our thought style. According to Mary Morgan, models function indeed as “in between objects between verbal narratives and statistical data.” In this view, not only economic models, but also fMRI experiments are neither employed to test our theories, nor to generate new knowledge. They serve as confirmation of and contributors to a narrative of the unrooted and fleeting parts of our knowledge which have not been rendered visible so far; they calculate the hitherto inexpressible, they found the previously inarguable.
I might be wrong, and, still, I want to go a step further: The notion that technological devices, statistical calculations, or modeling practices are crucial for so-called new discoveries, might reflect human insecurity which could stem from self-conscious awareness of our fallible perceptions. At this point, I want to get back to the nameless Professor’s self-playing instruments. I suspect they were seen to play a kind of music that no wo_man could produce, but maybe a seductive one, which was terrifying. When researchers use complex tools, especially those based on infallible mathematical principles, they also generate something no wo_man could produce, but these outputs do not seem scary. On the contrary, they are deemed more reliable than results acquired by human reasoning or perception. Researchers seem to hand the responsibility for validity over to their apparatuses. The sciences long for the instrument with the perfect performance, the creation of statistically provable knowledge with as little human interference as possible. These techno-mathematical tools seem to provide both the scientific narrative and the proof for its reliability.
So far, I have to admit, these claims are longing for a contextualized case study. But I really have to establish a relationship with my new computer2 now.
1 You can read about the same topic in one of her articles.
2 I named it “Extension,” term of endearment for “Extended Brain.” Guess why.