“How seriously should we take such speculations? Might machine learning bring us full-circle in the history of economic thought, to where measures of economic centralisation and control – condemned long ago as dangerous utopian schemes – return, boasting new levels of efficiency, to constitute a new orthodoxy?
…a great deal turns on the status of tacit knowledge. On this much the champions of a machine learning-powered revival of command economics and their critics agree. Tacit knowledge is the kind of cognition we refer to when we say that we know more than we can tell. How do you ride a bike? No one can say with any precision. Supervision helps, but a beginner has to figure it out for herself. How do you know that a spot is a freckle and not a cancer? A specialist cannot teach a medical student simply by spelling out her thinking in words. The student has to practise under supervision until she has mastered the skill for herself. This kind of know-how cannot be imparted or downloaded. Can robots assimilate tacit knowledge? artiftechnoloIf robots can retain tacit knowledge, AI-powered central planning might well outperform decentralised market interactions in coordinating economic activity. But there is good reason to believe that the mid-century anti-planners were right. Tacit knowledge will probably remain the preserve of human beings – with implications not only for the prospect of a return of the command economy, but also for broader fears and hopes about a future powered by machine learning…”
“The market’s strength is its capacity to aggregate available information and thereby equilibrate supply and demand. No single intelligence could possibly encompass all this information as effectively as the market mechanism. Individual knowledge was divided and fragmentary but, in the aggregate, the volume of information brought to bear upon the coordination of economic activity in and through the market is immense… If Lange was right, and relations between supply and demand in a given market could be formulated algebraically, the exponential growth of computer power made it only a matter of time before determining price centrally by Lange’s method became feasible.
Are not robots now doing what anti-planners said they could not – assimilating tacit knowledge?… Does the argument against computer-powered centralised planning that Hayek and Polanyi framed still apply? At first glance, machine learning suggests not. Cancer-spotting seems to involve algorithms emulating tacit knowledge. Medical students need years of book-learning and practical instruction by senior doctors before they can make the same discriminations. The inarticulate know-how that enables specialists to apply the relevant learning can be imparted only in person – that’s why, the world over, students follow doctors on ward rounds. A specialist doctor recognises malignant pigmentation but cannot articulate precisely what leads her to that conclusion. Now algorithms can perform the same feats of cognition. Are not robots now doing precisely what earlier anti-planners said they could not – assimilating tacit knowledge…”