C4SS Feed 44 presents David S. D’Amato‘s “Policy Research and the Limits of Statistical Utilitarianism” read by Dylan Delikta and edited by Nick Ford.
As an example of misguided attempts to reduce complex systems to something “legible,” “simplified,” and therefore controllable, James C. Scott describes the “scientific forestry” efforts of 18th and 19th century Germany, instituted to be statistically optimal, to maximize timber yields. Expert bureaucrats increasingly thought that they could plan aspects of social and economic life using metrics that they mistook for legitimate science. As Henry E. Lowood writes, “In the second half of the 18th century, few occupational groups rivaled government officials in their attention to numbers.” Ostensibly science- and data-driven, the German forests suffered from a host of problems, though these were not immediately apparent. In fact, for almost a century, it appeared that the forests had been a success. Only much later did the unintended consequences begin to manifest, did the hubris of the modernist’s shallow understanding of science and the capabilities of statistics reveal itself. As is so often the case, the libertarian will be reminded of Bastiat’s words: “In the economic sphere an act, a habit, an institution, a law produces not only one effect, but a series of effects. Of these effects, the first alone is immediate; it appears simultaneously with its cause; it is seen. The other effects emerge only subsequently; they are not seen; we are fortunate if we foresee them.” The Prussian effort at planning and managing forests for maximum productivity failed for the same reasons that planned economies fail. Economies, like forests, are complicated, composite phenomena, subsuming millions of smaller components, too complex to be doctored or controlled.
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