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Radical Uncertainty: Decision-Making Beyond…
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Radical Uncertainty: Decision-Making Beyond the Numbers (edição 2020)

por John Kay (Autor), Mervyn King (Autor)

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Membro:dhmontgomery
Título:Radical Uncertainty: Decision-Making Beyond the Numbers
Autores:John Kay (Autor)
Outros autores:Mervyn King (Autor)
Informação:W. W. Norton & Company (2020), 539 pages
Colecções:Nonfiction
Avaliação:****
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Radical Uncertainty: Decision-Making Beyond the Numbers por John Kay

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This was probably the most thought-provoking book I've read this year.

When I say that, I don't mean it was the kind of book that made me reconsider everything I believed, though it did some of that. Rather, nearly every page had me pausing to consider and mentally debate the book's arguments, which were always interesting even when I ultimately came down against the authors. (Of course, I have a larger-than-normal interest in epistemology and uncertainty, so your mileage may vary...)

The main point of the authors if to respond to an intellectual movement that claims to be foregrounding uncertainty in their analysis. This movement, inspired by Bayesian statistics, tries to get away from narrative-driven beliefs by instead quantifying the likelihood of those beliefs, and constantly updating those likelihoods in response to new information. The authors argue this movement is misguided, and actually does the opposite of what its backers claim it does — by quantifying risk, they say, people are applying false precision to things that are actually radically uncertain — impossible to quantify.

"Reasonable uncertainty is uncertainty which can be removed by looking something up (I am uncertain which city is the capital of Pennsylvania) or which can be represented by a known probability distribution of outcomes (the spin of a roulette wheel). With radical uncertainty, however, there is no similar means of resolving the uncertainty — we simply do not know."

Put another way, it is the difference between "risk" and "uncertainty" where "risk" means "unknowns which could be describe with probabilities" and "uncertainty" which can't. Today, the authors argue, we tend to treat uncertain things as if they are actually risks that can be precisely quantified. They give the example of national security advisers meeting with President Barack Obama in 2011, giving their assessments of whether Osama bin Laden was actually in a compound in Abbottabad, Pakistan — one advisor said there was a 95 percent chance bin Laden was there, while another said 80 percent and another 40 percent. Obviously these percentages are completely different from, say, the 50 percent chance that a fair coin will come up heads.

But this example also brings up one of the problems with the book: it's rather over-focused on issues inside the field of economics (and adjacent areas), and the author's arguments against various forms of probabilistic reasoning run into more issues when they move past critiquing over-quantified economic models and move to day-to-day decision-making.

To return to the prior example, in a very literal sense, the statement that there was a 95 percent chance Bin Laden was in Abbottabad is meaningless. Either he was there or he wasn't; it wasn't like you could raid the compound 20 times and expect to find Bin Laden 19 times. But this wasn't a case where the only options for belief were "he's there," "he's not there," and "we don't know." One can believe it is "more likely than not" that something is true, that evidence suggests something but doesn't prove it. Saying "95 percent" may not have any solid statistical basis, but isn't it a perfectly fine synonym for "almost certain"? To be sure, we need to make sure not to take that 95 percent estimate too seriously, as a real, empirical probability. But at a certain point, applied to real life and not economic models, this argument becomes a straw man.

Another favorite straw man argument the authors use is to mock the idea that actual people making real decisions have a "Bayesian dial floating over their heads" — a reference to the model of Bayesian statistics, which starts with a "prior probability" that something is true and then updates that probability based on evidence. Real decisions, they say, are based on narratives, not statistical models. Again, this is an argument that is obviously true in a very narrow sense — as they demonstrate, even professional economists and statisticians usually don't use their probabilistic methods for making life decisions — but falls down a bit when taken a little more loosely. It's perfectly possible to approach life in a pseudo-Bayesian sense, starting with your belief about what is the case, and updating it as you learn more, even if you're not actually constantly performing Bayesian math in your head like an imaginary person in an economic model.

But even if many of their arguments fall apart a bit when applied to real life and not to economics, this is still a helpful book for lay readers. Their emphasis on knowing when to say "I do not know" and the value of asking "What is going on here?" are well-taken. And their targets aren't just straw men — over-quantified economic models are real, and used as the basis for all sorts of hugely consequential decisions. (Among other things, they cite the bank models before the housing bubble burst in 2007-8, for which the collapse of the housing market allegedly involved "25-standard deviation moves several days in a row." As they note, "our universe has not existed long enough for there to have been days on which 25 standard deviation events could occur"; the problem was the models' assumptions, inputs and algorithms were wrong, and considered an event that actually did happen as basically impossible.) I think their points are made too strongly for laypeople's purposes, and are too focused on economics rather than daily life, but they're still well-taken. ( )
1 vote dhmontgomery | Dec 13, 2020 |
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