I was promised useful stories to assist me in a quest for justified belief. Instead I got a lesson in the limits of expertise. Unfortunately it was the author’s expertise that was limited.
>"My favorite form of epistemology is to pick a course that minimizes Black Swans. You’re less interested in truth than in survival. And I realize it’s a stretch to call it epistemology, but it’s very much a method for making decisions under uncertainty, which is kind of the whole point of having an epistemology."
Ha, you definitely pre-empted my objection. Perhaps one would say you care about epistemological triage?
Anyhow, thanks for the review. Epistemology is a dry topic, and it doesn't sound like the author moistened it much, but I enjoyed your bite sized overview of his contribution.
It's tempting to explore phrase licensing as a novel monetization method, but I give away enough words here you can take those two free of charge as well.
An excerpt from the latter: "Our consciousness expands in a new dimension when from mere experiencing we turn to the effort to understand what we have experienced. A third dimension of rationality emerges when the content of our acts of understanding is regarded as, of itself, a mere bright idea and we endeavor to settle what really is so. A fourth dimension comes to the fore when judgment on the facts is followed by deliberation on what we are to do about them."
Personally, I've found that Bayes' theorem is not as useful as its marketing by popular rationalists. (Note here I'm not arguing against limited application of Bayes, as the man himself might have endorsed, but on the idea of becoming a pure Bayesian calculator for everything ... or anything approaching that idea as an ideal.)
I used to buy into the model of iterative convergence on the truth through successive Bayesian calculations, at least as an approximation of what should happen in an ideal world, even if you couldn't run the calculations on each new round of information in practice. Then the rootclaim COVID debate shone a light on the problems with that approach:
1. In order to use Bayes, you need to know 3 variables.
2. Most people know 1 and guess the other 2.
3. Two people may disagree, from the same data, wildly about their guesses.
4. Formalizing the math and comparing these two operations reveals the folly of using Bayes when you can only guess at unknown variables.
At the end of the debate, the two sides weren't directionally pointing the same way, and their mathematical 'certainly' was many more orders of magnitude apart after viewing the evidence than before.
This fits research that indicates people will take the same data and become more polarized. Ironically, this new evidence has not swayed the rationalists.
If there's one area where rationalists have not just gone astray, but led others down the primrose path of error, it's the hand waving toward Bayes' theorem as a magical staff of truth. It gives them a false sense of precision to their arguments. Bayes is useful when you can objectively observe the known variables. Otherwise, Bayes' theorem is a dangerous habit to get into.
I agree that the rootclaim COVID debate was kind of a disaster.
I also agree that Bayes' is very limited. And not especially useful.
The key thing I was trying to get across was not that Bayes' was far superior to the methodology in the book. (I think it might be marginally superior.) But my bafflement that the book didn't even mention it as an alternative. That seemed to be bespeak exactly the sort of siloing the book was urging people to be very careful about.
This was great as usual
>"My favorite form of epistemology is to pick a course that minimizes Black Swans. You’re less interested in truth than in survival. And I realize it’s a stretch to call it epistemology, but it’s very much a method for making decisions under uncertainty, which is kind of the whole point of having an epistemology."
Ha, you definitely pre-empted my objection. Perhaps one would say you care about epistemological triage?
Anyhow, thanks for the review. Epistemology is a dry topic, and it doesn't sound like the author moistened it much, but I enjoyed your bite sized overview of his contribution.
"Epistemological triage" is a great phrase, I'm going to have to steal it.
It's tempting to explore phrase licensing as a novel monetization method, but I give away enough words here you can take those two free of charge as well.
Very well written. Perhaps another course that might lead us towards capital-T truth is through the cognitional theory of twentieth-century Jesuit philosopher and theologian Bernard Lonergan. Below are two articles that attack it from very different directions, but each wrestle with the issue gracefully: https://churchlifejournal.nd.edu/articles/bernard-lonergans-cognitive-structure-for-ethical-ai/ & https://knownunknown.wordpress.com/writings-3/cognitional-theory/
An excerpt from the latter: "Our consciousness expands in a new dimension when from mere experiencing we turn to the effort to understand what we have experienced. A third dimension of rationality emerges when the content of our acts of understanding is regarded as, of itself, a mere bright idea and we endeavor to settle what really is so. A fourth dimension comes to the fore when judgment on the facts is followed by deliberation on what we are to do about them."
Personally, I've found that Bayes' theorem is not as useful as its marketing by popular rationalists. (Note here I'm not arguing against limited application of Bayes, as the man himself might have endorsed, but on the idea of becoming a pure Bayesian calculator for everything ... or anything approaching that idea as an ideal.)
I used to buy into the model of iterative convergence on the truth through successive Bayesian calculations, at least as an approximation of what should happen in an ideal world, even if you couldn't run the calculations on each new round of information in practice. Then the rootclaim COVID debate shone a light on the problems with that approach:
1. In order to use Bayes, you need to know 3 variables.
2. Most people know 1 and guess the other 2.
3. Two people may disagree, from the same data, wildly about their guesses.
4. Formalizing the math and comparing these two operations reveals the folly of using Bayes when you can only guess at unknown variables.
At the end of the debate, the two sides weren't directionally pointing the same way, and their mathematical 'certainly' was many more orders of magnitude apart after viewing the evidence than before.
This fits research that indicates people will take the same data and become more polarized. Ironically, this new evidence has not swayed the rationalists.
If there's one area where rationalists have not just gone astray, but led others down the primrose path of error, it's the hand waving toward Bayes' theorem as a magical staff of truth. It gives them a false sense of precision to their arguments. Bayes is useful when you can objectively observe the known variables. Otherwise, Bayes' theorem is a dangerous habit to get into.
I agree that the rootclaim COVID debate was kind of a disaster.
I also agree that Bayes' is very limited. And not especially useful.
The key thing I was trying to get across was not that Bayes' was far superior to the methodology in the book. (I think it might be marginally superior.) But my bafflement that the book didn't even mention it as an alternative. That seemed to be bespeak exactly the sort of siloing the book was urging people to be very careful about.