Following up on my previous post, I want to explain the logic of Scott Scheall’s explanation of Elon Musk’s failed DOGE project, or what I like to call Scheall’s theorem, which can be summed up in three words: goals require knowledge. Or in Scheall’s words: “To deliberately realize goal G, policymakers must possess knowledge K. If policymakers lack knowledge K, they cannot deliberately realize goal G.” That is, in order to achieve some desired outcome or objective, a person or firm must have some level of know-how about what steps to take, and in what sequence, in order to accomplish his aims. Stated in formal terms, we can say that the probability that an actor A will achieve a given goal G is a function of A’s level of knowledge K. Or stated even more simply: K is a necessary, though not a sufficient, condition of G.
What is so beautiful about Scheall’s theorem is not just its simplicity but also its generality, for we can extend the logic of Scheall’s argument to the government. By way of illustration, we might want the government to accomplish some lofty goal, such as the eradication of poverty or the reduction of greenhouse gases or the elimination of wasteful spending, and the government, in turn, might have the power to issue orders towards those ends — what I like to call the “pretense of power” in honor of F. A. Hayek (see here or here, for example). But without sufficient knowledge about how to actually carry out its commands, it is highly unlikely that such goals, however well-intentioned, will actually be accomplished in a cost-effective or timely manner. Given Scheall’s theorem, the surprise is not that Elon Musk’s DOGE project failed; instead, the surprise for me is that it failed so quickly: DOGE was quietly shut down within eight months of its creation.
prior probability 