Thanks for the feedback :)
The twelve virtues are already linked in the article, but I hadn't come across the Basics of Rationalist Discourse yet. It is indeed very interesting and aligned with the objective of this essay. I very much intend to incorporate its content where applicable and give it credit (I will link it at the beginning of the post).
I wonder if the Basics of Rationalist Discourse doesn't represent a more detailed and tactical view of the topic, as opposed to the more generic top-of-mind principles that this essay tried to cover. The former is a manual for controlling specific behaviours, the latter a memento of the general mindset to hold during a debate. Does this seem reasonable to you?
As per your question, I think my starting point of view was of a debate with a clear objective, a "primary question" that the group is trying to answer. If the purpose of the debate is entertainment, then there might be little cost in deviating from the main question (except probably frustrating some of the other participants). But if indeed there is value in the timeliness and accuracy of the question (e.g., in a business setting), then focus is probably applicable.
(As to whether exploring unrelated ideas can in fact improve the outcome of the debate - which is very plausible - that feels like an activity outside the confines of the debate and possibly better left for before the actual debate).
My argument was never that abduction is a subset of induction, but that it can always be replaced by a combination of deduction and induction.
The effect of simplicity and consistency on probabilities can both be classified as deductions:
The underlying issue is that what we are trying to do with abduction is find the hidden mechanism behind the directly observable, the force of gravity that makes the apple fall. Since induction is limited to inferring futures observations from past ones, it is limited to the observable and silent about behind-the-scenes mechanisms. And so it is limited compared to abduction, and so abduction is not a form of induction.
I don't think this is generally correct. Induction is about moving probability mass to both parameters and models that best explain the evidence. So it both improves your existing models and makes you choose better models (aka new "mechanisms" as you call them).
(This is a Machine Learning friendly way to see induction; more generally, you could consider any model-parameter combination as a separate model.)
Bayes allow you to confirm hypotheses that would generate the observable evidence, but doesn't mechanically generate them for you , and also.doesn't allow you to distinguish equally predictive ones. You can solve the first problem by creatively positing hypotheses, and the second with the criteria of simplicity of and consistency. That gives you full abductive reasoning . Bayes is a subset of full abductive reasoning.
I also don't think this is quite correct. Simplicity and consistency should be considered evidence in your application of Bayes' theorem. Namely, Bayes is complete: there is no other theorem or formula required to achieve the most accurate estimate of probability for beliefs. (Also, Bayesian statistics is considered a subset of inferential statistics, which is the formal mathematics associated with induction. Whether you think Bayes Theorem itself fits under induction or deduction, I don't think most people would consider it abduction).
Besides this, if I understand correctly, you are proposing that the core of abduction is about generating hypotheses rather than evaluating the evidence for or against them. This I find intriguing.
I was originally considering the standpoint of an "optimal Bayesian" who simultaneously evaluates all hypotheses at once by shifting probability mass, but this is far from the human experience.
I do wonder whether this still happens subconsciously or whether hypothesis search constitutes in some way its own form of reasoning. But I'm afraid I haven't thought enough about this, so I won't be able to argue about it.
Thank you for the inspiration though, quite useful.
I am not sure what your definition of Bayesian reasoning is, but I personally think of:
I am not sure where most people would place Bayes' theorem itself, but Bayesian statistics is usually considered a subfield of (or rather an approach to) inferential statistics.
Apologies, I am not sure I understood.
Is it that:
- Induction/Deduction fit data to existing models
- Abduction is about proposing new models?
I think "best" is a bit of a generic term, but in regard to beliefs, it seems safe to say that the "quality" of a belief should be judged exclusively on its probability of being true.
Simplicity and consistency are then factors that positively affect the probability of a belief being true, but they are not in themselves determinants of the "best" explanation.
Given:
- Belief A, which is 51% likely to be true but very complex and somewhat discordant with existing beliefs (this is included in the probability estimation)
- Belief B, which is 49% likely to be true but very simple and elegant
Assuming the probabilities are correct, I think Belief A should be considered the "best". Would you agree with this?
P.S. Quantum field theory is an example of a very complex theory and inconsistent with other accepted theories (e.g. General Relativity), but still the "best" explanation for empirical evidence.
Yes indeed.
There is still - almost always - an (arbitrarily) small chance that even "ridiculous" statements turn out to be true.
E.g., maybe the Earth IS flat, and we have just been brainwashed by an advanced alien race to perceive physics such that it appears spherical. Very very very very unlikely, but not impossible.
Note that from Bayes' Theorem, if your prior is either 0 or 1, then no amount of evidence can change it. Which for a rationalist is a horrible position to be in, since it basically means that you have stopped learning and your beliefs are now disentangled from reality.