Bayesian Cybernetics

The posthumous discovery of Thomas Bayes’s Essay towards Solving A Problem In The Doctrine Of Chances (1761), and its rigorous rule for the revision of probabilistic inferences in response to emerging evidence, brought risk analysis to a level of comprehensiveness that was fully epistemological, and thus no longer subordinate–even nominally– to higher-order determinations of knowledge. In Bayesian adaptive forecasting, a circuit was completed. Modernity had learnt how to think risk, and thinking risk had taught it how to learn. What it had learnt and what it had risked were no longer meaningfully distinguishable. It had realized integral cognitive hazard, or virtual intelligence catastrophe.

Land, Nick. “Odds and Ends: On Ultimate Risk.” Collapse VIII. Urbanomics (2014).

During the last 200 years the laws of probability, Arabic numbers, compound interest, and the limited liability company were successfully integrated into a distributed system of wagers that progressively took over the world. To truly bear witness the insanity of modernity is to get lost in an incomprehensible cyclone of complexity; most people are lulled into a sort of perpetual future-shock. It is hard to deny the brilliance of the minds that created those powerful engines that we use nearly every day to go to work, those flying marvels that take us from one side of the world to another with almost miraculous reliability, or the ability to be seen and heard nearly everywhere in the world by just clicking a few buttons. The system is based on an intrinsic reward to wagers, by definition risks must be compensated if they are to be taken. For centuries, what was once called the capitalist class used their wealth to undertake ventures, dramatically when it was close to running out, by investing in some offshore interest. And so, with the passing of time, nearly all of the world is in some way generating or prospecting for productivity, with all sorts of stakes attached to these. And as a greater and greater edifice is built upon these wagers, colossal against an increasingly unsympathetic planet, one can start to see it teetering like a tower that as a child one would build, swaying softly after the placement of the last block. It is not without reason that our ancestors feared change so much. And doubly so now, as we must not only navigate through change but also have our wagers succeed also.

What I want to highlight here is the following premise: “In Bayesian adaptive forecasting, a circuit was completed. Modernity had learnt how to think risk, and thinking risk had taught it how to learn.” In no time is this truer than today, where the machine learning revolution, as well as the increasing wealth of information that we are exposed to, has not only added a new dimension to the phenomena that can be studied–economically, sociologically and psychologically–but, crucially, an impulse to progressively integrate these horizontal extension of measurement-of-everything into our objectivity. As we transition to a post-structural world and ever greater parts of our identity are digitized (more precisely, those that can be measured), we too wager the that the very things we measure are forever changed through measurement and the feedback we direct through and by these measures. The Bayesian notion of updating priors to update expectations continuously with the arrival of new data insinuates a feedback cycle that perpetuates measurement and prediction that in itself resists objective delineation, never mind understanding. This incredible tool gets sharper each time you use it, and if you’re competing with others, you have every incentive to use it as much as possible. If modernity is truly defined as a risky wager then in assuming this risk we must really evaluate emergent risks that arise through the systemic undertaking of risk itself. The calculation of risk, as a cultural innovation whose real coherence is expressed as an emergent being, or developing global system, is unable to step outside itself, in order to submit to an objective self-estimation. More concretely, systems that we cannot understand cannot be measured. The unknown unknown is not the consequence of a single wager–which unless measured and deemed correspondingly rewarding is not taken–but the joint emergence of a system-of-wagers and the risk that pertains to itself, which is unmeasurable and unknowable.

5 thoughts on “Bayesian Cybernetics”

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