Sorry to break your admonition, but without weighing in on the drug itself, I say there's a good Hayekean reason to agree that "clinicians who are saving patients using Ivermectin should not have their evidence over-ruled by randomized controlled trials"
An RCT is a mechanism by which society attempts to reach consensus on captial-K Knowledge. But an experienced clinician developing a treatment protocol is appying situational practical knowledge.
If that protocol includes a possibly useless but well-known-to-be-safe drug, then society doesn't need to enforce its consensus on him. It can just get out if the way and let him do his job.
How is a biologist being very critical of an "over processed/over mathed" scientific result due to both empirical evidence from practitioners and serious methodology concerns different from an economist being very critical of an "over processed/over mathed" policy proposal due to common sense axioms and real life business experience?
I'm not in a position to agree/disagree with Bret's takedown of the "Together trial" that he did in an extensive podcast, but it sounds a lot like Arnold railing against the MIT school's economics outputs.
My high school chemistry teacher liked to talk about the distinction between accuracy and precision.
Math is a language of precise description. Use of math substantially increases the odds that researchers are at least talking about the same thing. This is rather valuable in a global research community.
However, a precise description is not necessarily an accurate one. And accuracy is ultimately what matters most.
The big question is, what practices would improve accuracy in the economics community? I don't think getting rid of math is the answer; I believe math is still part of the puzzle, it's just of secondary rather than primary importance.
Brett seems correct: "Audacity, Tenacity, Veracity" - more important than mental horsepower.
Care about truth.
Willing to say stuff others don't like.
Willing to stick with the problem puzzle until it is solved.
Steve thinks "the problem with models is that they may be too simple for the processes they are trying to model. I [Arnold] think that is a big problem in economics. "
The over simple model, like Econ 101 (from Michael Lind) is certainly one problem.
But the biggest problem is the goal of economics. Is it to understand? Or to influence, to control, to improve? Or to profit thru investments or other actions because of the econ?
Simple models are likely OK for simplified understanding, but no amount of econ understanding is enough to control thinking humans who constantly choose to be uncontrollable.
“But don’t give up just because he said one thing that you or I think is wrong. And don’t waste your comments on this issue, because it is not central to the podcast.”
I find this approach to learning from people extremely underrated by my generation. For instance, if I try to recommend Zero to One to someone they want to scream about how very bad Thiel’s politics are before they consider even a single piece of insight he has on the topic we’re discussing. I want to scream that of course he thinks differently from conventional wisdom because that’s what the entire book is about! Should I be trying to learn how to found a radically different company from someone who thinks the same as everyone else?
That overly specific example asides, we put all these prerequisites on people having to think X and Y in order to deserve any respect at all and it’s totally antithetical to what true learning is about.
Randomized Clinical Trials (RCTs) can easily be rigged to produce a desired result, and this was in fact done repeatedly during the pandemic to suppress the use of re-purposed anti-viral agents in order to protect the conditions for Emergency Use Authorization of the vaccines, among which is that there be no alternative therapy available. Under such circumstances the experience of practitioners may be of better evidentiary value than manipulated RCTs. In fact, one RCT published by the prestigious journal Lancet had to be withdrawn when the data base was found to be entirely fraudulent, something that should have been checked before publication but wasn't due to the urgency of publishing in support of the official Public Health narrative. Besides outright fraud, it is easy to manipulate dosage, timing of treatment, etc., to get an intended negative result, and this was also done. (There were also many RCTs from other countries strongly supporting the use of re-purposed drugs).
By way of contrast, practitioners Dr. George Fareed and Dr. Brian Tyson successfully treated thousands of Covid cases with re-purposed drugs with almost no adverse outcomes from the disease among a highly vulnerable population in their chain of urgent care centers in California's Imperial Valley.
My sense, greatly reinforced by the COVID experience, isn't so much that the models are bad, but the data is generally terrible. At best it's a wild guess, but much more commonly data elements are selected to prove the point at issue.
I tend to think something like this: Solow made his living as a scientist, constructing illustrative models of how the world worked. His successors make their livings as engineers, providing results based on application of his models. But... the results these guys get aren't really used for anything more than political talking points, so the answer is going to be "whatever we want it to be".
I think Patterson worth listening to, but also has something of the too-cocky autodidact about him.
He believes that flaws in the foundations of mathematics and corrupt everything. Cantor's diagonal argument somehow allows scaremingers to abuse the word "exponential" during Covid. How?
Sorry to break your admonition, but without weighing in on the drug itself, I say there's a good Hayekean reason to agree that "clinicians who are saving patients using Ivermectin should not have their evidence over-ruled by randomized controlled trials"
An RCT is a mechanism by which society attempts to reach consensus on captial-K Knowledge. But an experienced clinician developing a treatment protocol is appying situational practical knowledge.
If that protocol includes a possibly useless but well-known-to-be-safe drug, then society doesn't need to enforce its consensus on him. It can just get out if the way and let him do his job.
Wherever science and politics intersect, science is bent and corrupted.
Podcast was excellent, raised my view of Weinstein again
How is a biologist being very critical of an "over processed/over mathed" scientific result due to both empirical evidence from practitioners and serious methodology concerns different from an economist being very critical of an "over processed/over mathed" policy proposal due to common sense axioms and real life business experience?
I'm not in a position to agree/disagree with Bret's takedown of the "Together trial" that he did in an extensive podcast, but it sounds a lot like Arnold railing against the MIT school's economics outputs.
My high school chemistry teacher liked to talk about the distinction between accuracy and precision.
Math is a language of precise description. Use of math substantially increases the odds that researchers are at least talking about the same thing. This is rather valuable in a global research community.
However, a precise description is not necessarily an accurate one. And accuracy is ultimately what matters most.
The big question is, what practices would improve accuracy in the economics community? I don't think getting rid of math is the answer; I believe math is still part of the puzzle, it's just of secondary rather than primary importance.
Etienne Gilson made much the same point: “the conclusions of the master are the premises of the disciple.”
Brett seems correct: "Audacity, Tenacity, Veracity" - more important than mental horsepower.
Care about truth.
Willing to say stuff others don't like.
Willing to stick with the problem puzzle until it is solved.
Steve thinks "the problem with models is that they may be too simple for the processes they are trying to model. I [Arnold] think that is a big problem in economics. "
The over simple model, like Econ 101 (from Michael Lind) is certainly one problem.
But the biggest problem is the goal of economics. Is it to understand? Or to influence, to control, to improve? Or to profit thru investments or other actions because of the econ?
Simple models are likely OK for simplified understanding, but no amount of econ understanding is enough to control thinking humans who constantly choose to be uncontrollable.
“But don’t give up just because he said one thing that you or I think is wrong. And don’t waste your comments on this issue, because it is not central to the podcast.”
I find this approach to learning from people extremely underrated by my generation. For instance, if I try to recommend Zero to One to someone they want to scream about how very bad Thiel’s politics are before they consider even a single piece of insight he has on the topic we’re discussing. I want to scream that of course he thinks differently from conventional wisdom because that’s what the entire book is about! Should I be trying to learn how to found a radically different company from someone who thinks the same as everyone else?
That overly specific example asides, we put all these prerequisites on people having to think X and Y in order to deserve any respect at all and it’s totally antithetical to what true learning is about.
Randomized Clinical Trials (RCTs) can easily be rigged to produce a desired result, and this was in fact done repeatedly during the pandemic to suppress the use of re-purposed anti-viral agents in order to protect the conditions for Emergency Use Authorization of the vaccines, among which is that there be no alternative therapy available. Under such circumstances the experience of practitioners may be of better evidentiary value than manipulated RCTs. In fact, one RCT published by the prestigious journal Lancet had to be withdrawn when the data base was found to be entirely fraudulent, something that should have been checked before publication but wasn't due to the urgency of publishing in support of the official Public Health narrative. Besides outright fraud, it is easy to manipulate dosage, timing of treatment, etc., to get an intended negative result, and this was also done. (There were also many RCTs from other countries strongly supporting the use of re-purposed drugs).
By way of contrast, practitioners Dr. George Fareed and Dr. Brian Tyson successfully treated thousands of Covid cases with re-purposed drugs with almost no adverse outcomes from the disease among a highly vulnerable population in their chain of urgent care centers in California's Imperial Valley.
My sense, greatly reinforced by the COVID experience, isn't so much that the models are bad, but the data is generally terrible. At best it's a wild guess, but much more commonly data elements are selected to prove the point at issue.
I tend to think something like this: Solow made his living as a scientist, constructing illustrative models of how the world worked. His successors make their livings as engineers, providing results based on application of his models. But... the results these guys get aren't really used for anything more than political talking points, so the answer is going to be "whatever we want it to be".
I think Patterson worth listening to, but also has something of the too-cocky autodidact about him.
He believes that flaws in the foundations of mathematics and corrupt everything. Cantor's diagonal argument somehow allows scaremingers to abuse the word "exponential" during Covid. How?
When you dig into the claimed flaws, we find bald assertions. He doesn't believe infinite sets. And his argument is basically "they are an obvious absurdity, QED". https://steve-patterson.com/infinite-things-do-not-exist/