The inability to make any sort of useful predictions about the future is a problem, both practically and as a matter of scientific method.
Alexander Rosenberg’s Blunt Instrument is a provocation, by intention. Its central claim starts with the recognition that economics systematically fails to predict real-world outcomes. But then Rosenberg goes on to argue that economic theory, in its classic rational-choice and equilibrium form, is structurally incapable of becoming a genuinely predictive science. Economists have spent more than a century elaborating progressively more elegant—and, to be honest, more interesting—mathematical models. The problem is that economists can only predict the past, especially the recent past, using these models. The inability to make any sort of useful predictions about the future is a problem, both practically and as a matter of scientific method.
Yet the book is not an outsider’s uninformed attack, because Rosenberg knows economists’ methods and approach. (Candor requires that I disclose that Rosenberg is a longtime friend, and someone with whom I co-teach classes in the “PPE” program he helped found. His Routledge book, The Philosophy of Social Science, is now in its fifth edition, and has nearly 2,500 citations). Blunt Instrument’s claim is more interesting and constructive: despite the manifest failures of economics and the limits even on its successes, economic theory is indispensable. Economics gave rise to game theory and mechanism design, the tools we need for designing institutions that can cope with strategic, self-interested human behavior. It is more appropriate, in Rosenberg’s view, to think of economics not as a science of prediction, but rather a practical instrument of institutional diagnosis and self-defense.
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We can predict another i at the same t pretty well, but predicting a future t even for the same i is much harder.
failure?
TBH, I've always found the criticism that economics "can't predict" to be a bit strange. I don't know many modern economists who claim that our models are highly predictive. Most think of them as helping us to understand better, and thus make better predictions. What helps us make better predictions is the better understanding, not that we follow the mathematical slavishly. And even at places like the Fed, that's how it works. Model results are only one input among many, including human insight and experience. Feels like they're attacking a straw man.
Sometimes we can