If fallacies are weak Bayesian evidence, and given that for just about any fallacy there is a fallacy that simply negates output of fallacy (a fancy fallacious reasoner’s fallacy), then how come fallacies don’t (mostly) cancel out as evidence?
e.g. practical example: “correlation implies [direct] causation” is the simple fallacy, and “correlation doesn’t imply any correlation” is the corresponding fancy fallacy. Which is also wrong because in our universe, unless it’s QM, if a strongly correlates with b, then either a causes b, b causes a, or c causes both a and b—it’s not that there’s no causation, it’s that causation may not be the one you privileged. Usually when you try to teach everyone about some fallacy, you end up creating another fallacy that’s opposite (or a bias).
One needs to somehow gauge the ‘fallaciousness’ of opposite fallacies.
“One needs to somehow gauge the ‘fallaciousness’ of opposite fallacies.”
Isn’t that exactly what the Hahn-Oaksford paper does? I doubt I’m as intelligent as most people on this site, but I was under the impression that this was all about using Bayesian methods to measure the probable “fallaciousness” of certain informal fallacies.
I think what happens is that informal and fallacious reasoning rapidly (exponentially or super exponentially in number of steps) diverges from making sense, so it’s weight as evidence is typically extremely close to zero.
If fallacies are weak Bayesian evidence, and given that for just about any fallacy there is a fallacy that simply negates output of fallacy (a fancy fallacious reasoner’s fallacy), then how come fallacies don’t (mostly) cancel out as evidence?
e.g. practical example: “correlation implies [direct] causation” is the simple fallacy, and “correlation doesn’t imply any correlation” is the corresponding fancy fallacy. Which is also wrong because in our universe, unless it’s QM, if a strongly correlates with b, then either a causes b, b causes a, or c causes both a and b—it’s not that there’s no causation, it’s that causation may not be the one you privileged. Usually when you try to teach everyone about some fallacy, you end up creating another fallacy that’s opposite (or a bias).
One needs to somehow gauge the ‘fallaciousness’ of opposite fallacies.
Isn’t that exactly what the Hahn-Oaksford paper does? I doubt I’m as intelligent as most people on this site, but I was under the impression that this was all about using Bayesian methods to measure the probable “fallaciousness” of certain informal fallacies.
I think what happens is that informal and fallacious reasoning rapidly (exponentially or super exponentially in number of steps) diverges from making sense, so it’s weight as evidence is typically extremely close to zero.