Computational thought, the idea that information from the past gives us knowledge of the future is the source of most if not all of the worlds existential threats. This is the crux of the argument James Bridle makes in ‘New Dark Age: Technology and the End of the Future’. Bridles argument uses many examples of scientific reductionism as sources for the evolution of computational thought and culture. His best illustration of the problem was in his first chapter relating to the prediction of weather and rise of computational thinking in chapter two. As I read through his history of scientific reductionism as a way of knowing and predicting the future through models of complex information I was struck by the underlying phenomenon in every example. The assumption that all knowledge reduced to its basic form is aesthetically true and useful. Bridle does not make this statement, if anything he argues for the concept of unknowing as a form of thinking which allows one to cut through complexity and see the ‘network’ for what it is. However, it does seem paradoxical that one solution to seeing true complexity and interconnectedness as opposed to reductive thinking, is to reduce one’s thinking? In this case to the here and now. I believe the concept has merit, I’ve noticed in my own research that reductionism in information systems tends to multiply the information and complexity as opposed to simplifying it, a phenomenon which goes hand in hand with computational thinking according to Bridle. His mention of Lewis Fry Richardson’s ‘coastline paradox’ struck me as the most compelling example partly because I had just read the exact same account in relation to my own research with regards to finding “true numbers” in measurement.
Bridles continued use of examples involving aviation struck me as particularly timely considering the automation systems placed in the Boeing 737 max E without much thought for educating pilots in their use or potential ways to counter the systems when they fail. Ironically, perhaps morbidly in light of Bridles argument, the supposed “fix” is a software patch with more sensors for more information and automation, while training pilots on a simulator so simple it can be used on an IPAD. The juxtaposition of this example in the real world news cycle while reading the book with Bridle’s focus on aviation examples and automation bias combined with computational thinking was enough to make me believe that this book should be the first text book covered in any science, computer, or engineering based program.
The automation bias and confirmation bias examples beg the question that underlies Bridles entire book, a question I’ve come across in my own research on Processing Fluency and Aesthetics. If we are entwined in the network and computational thinking is pervasive while at the same time we are compelled by our own mental biases to favor the simple and immediate solutions over the complex and hard, How do we reverse or engineer around the process? Bridle’s literacy/fluency with unknowing solution seems like an example of what he’s arguing against. It’s too simple, too convenient. I do see it as an approach and perhaps the proper mindset to, at least combat the tendency toward reductionism while believing the information has the answer. It all reminds me of the Buddhist idea that there is no answer. Being in the present (not even thinking, just existing in the case of Buddhism) is supposed to be enough, but that only works if everyone is Buddhist, or in Bridle’s example, if everyone avoids the computational thinking trap. His final plea, “We only have to think, and think again, and keep thinking” seems reductionist and like a nice hopeful sentence to end on, yet I think he was suffering from his own mental biases in reducing the problem to some sort of action, thinking. Thinking is what got us here. IBM’s motto “Think” in all over its reductionist glory, seems like the same solution Bridle proposes, yet a solution which was proposed over 100 years ago and is easily shown to be part of the problem Bridle argues against. Fight fire with fire I suppose.