It seems odd to equate rationality with probabilistic reasoning. Philosophers have always distinguished between demonstrative (i.e., mathematical) reasoning and probabilistic (i.e., empirical) reasoning. To say that rationality is constituted only by the latter form reasoning is very odd, especially considering that it is only though demonstrative knowledge that we can even formulate such things as Bayesian mathematics.
Category theory is a meta-theory of demonstrative knowledge. It helps us understand how concepts relate to each other in a rigorous way. This helps with the theory side of science rather than the observation side of science (although applied category theories are working to build unified formalisms for experiments-as-events and theories).
I think it is accurate to say that, outside of computer science, applied category theory is a very young field (maybe 10-20 years old). It is not surprising that there haven’t been major breakthroughs yet. Historically fruitful applications of discoveries in pure math often take decades or even centuries to develop. The wave equation was discovered in the 1750s in a pure math context, but it wasn’t until the 1860s that Maxwell used it to develop a theory of electromagnetism. Of course, this is not in itself an argument that CT will produce applied breakthroughs. However, we can draw a kind of meta-historical generalization that mathematical theories which are central/profound to pure mathematicians often turn out to be useful in describing the world (Ian Stewart sketches this argument in his Concepts of Modern Mathematics pp 6-7).
CT is one of the key ideas in 20th century algebra/topology/logic which has allowed huge innovation in modern mathematics. What I find interesting in particular about CT is how it allows problems to be translated between universes of discourse. I think a lot of its promise in science may be in a similar vein. Imagine if scientists across different scientific disciplines had a way to use the theoretical insights of other disciplines to attack their problems. We already see this when say economists borrow equations from physics, but CT could enable a more systematic sharing of theoretical apparatus across scientific domains.
Under the paradigm of probability as extended logic, it is wrong to distinguish between empirical and demonstrative reasoning, since classical logic is just the limit of Bayesian probability with probabilities 0 and 1.
Besides that, category theory was born more than 70 years ago! Sure, very young compared to other disciplines, but not *so* young. Also, the work of Lawvere (the first to connect categories and logic) began in the 70′s, so it dates at least forty years back.
That said, I’m not saying that category theory cannot in principle be used to reason about reasoning (the effective topos is a wonderful piece of machinery), it just cannot say that much right now about Bayesian reasoning
Interesting. This might be somewhat off topic, but I’m curious how would such an Bayesian analysis of mathematical knowledge explain the fact that it is provable that any number of randomly selected real numbers are non-computable with a probability 1, yet this is not equivalent to a proof that all real numbers are non-computable. The real numbers 1, 1.4, square root 2, pi, etc are all computable numbers, although the probability of such numbers occurring in an empirical sample of the domain is zero.
So far, Bayesian probability has been extended to infinite sets only as a limit of continuous transfinite functions. So I’m not quite sure of the official answer to that question.
On the other hand, what I know is that even common measure theory cannot talk about the probability of a singleton if the support is continuous: no sigma-algebra on 2ℵ0 supports the atomic elements.
And if you’re willing to bite the bullet, and define such an algebra through the use of a measurable cardinal, you end up with an ultrafilter that allows you to define infinitesimal quantities
I don’t know enough math to understand your response. However, from the bits I can understand, it seems leave open the epistemic issue of needing an account of demostrative knowledge that is not dependent on Bayesian probability.
It seems odd to equate rationality with probabilistic reasoning. Philosophers have always distinguished between demonstrative (i.e., mathematical) reasoning and probabilistic (i.e., empirical) reasoning. To say that rationality is constituted only by the latter form reasoning is very odd, especially considering that it is only though demonstrative knowledge that we can even formulate such things as Bayesian mathematics.
Category theory is a meta-theory of demonstrative knowledge. It helps us understand how concepts relate to each other in a rigorous way. This helps with the theory side of science rather than the observation side of science (although applied category theories are working to build unified formalisms for experiments-as-events and theories).
I think it is accurate to say that, outside of computer science, applied category theory is a very young field (maybe 10-20 years old). It is not surprising that there haven’t been major breakthroughs yet. Historically fruitful applications of discoveries in pure math often take decades or even centuries to develop. The wave equation was discovered in the 1750s in a pure math context, but it wasn’t until the 1860s that Maxwell used it to develop a theory of electromagnetism. Of course, this is not in itself an argument that CT will produce applied breakthroughs. However, we can draw a kind of meta-historical generalization that mathematical theories which are central/profound to pure mathematicians often turn out to be useful in describing the world (Ian Stewart sketches this argument in his Concepts of Modern Mathematics pp 6-7).
CT is one of the key ideas in 20th century algebra/topology/logic which has allowed huge innovation in modern mathematics. What I find interesting in particular about CT is how it allows problems to be translated between universes of discourse. I think a lot of its promise in science may be in a similar vein. Imagine if scientists across different scientific disciplines had a way to use the theoretical insights of other disciplines to attack their problems. We already see this when say economists borrow equations from physics, but CT could enable a more systematic sharing of theoretical apparatus across scientific domains.
Under the paradigm of probability as extended logic, it is wrong to distinguish between empirical and demonstrative reasoning, since classical logic is just the limit of Bayesian probability with probabilities 0 and 1.
Besides that, category theory was born more than 70 years ago! Sure, very young compared to other disciplines, but not *so* young. Also, the work of Lawvere (the first to connect categories and logic) began in the 70′s, so it dates at least forty years back.
That said, I’m not saying that category theory cannot in principle be used to reason about reasoning (the effective topos is a wonderful piece of machinery), it just cannot say that much right now about Bayesian reasoning
Interesting. This might be somewhat off topic, but I’m curious how would such an Bayesian analysis of mathematical knowledge explain the fact that it is provable that any number of randomly selected real numbers are non-computable with a probability 1, yet this is not equivalent to a proof that all real numbers are non-computable. The real numbers 1, 1.4, square root 2, pi, etc are all computable numbers, although the probability of such numbers occurring in an empirical sample of the domain is zero.
So far, Bayesian probability has been extended to infinite sets only as a limit of continuous transfinite functions. So I’m not quite sure of the official answer to that question.
On the other hand, what I know is that even common measure theory cannot talk about the probability of a singleton if the support is continuous: no sigma-algebra on 2ℵ0 supports the atomic elements.
And if you’re willing to bite the bullet, and define such an algebra through the use of a measurable cardinal, you end up with an ultrafilter that allows you to define infinitesimal quantities
I don’t know enough math to understand your response. However, from the bits I can understand, it seems leave open the epistemic issue of needing an account of demostrative knowledge that is not dependent on Bayesian probability.