What math is essential to the art of rationality?

Capla

I have started to put together a sort of curriculum for learning the subjects that lend themselves to rationality. It includes things like experimental methodology and cognitive psychology (obviously), along with "support disciplines" like computer science and economics. I think (though maybe I'm wrong) that mathematics is one of the most important things to understand.

Eliezer said in the simple math of everything:

It seems to me that there's a substantial advantage in knowing the drop-dead basic fundamental embarrassingly simple mathematics in as many different subjects as you can manage. Not, necessarily, the high-falutin' complicated damn math that appears in the latest journal articles. Not unless you plan to become a professional in the field. But for people who can read calculus, and sometimes just plain algebra, the drop-dead basic mathematics of a field may not take that long to learn. And it's likely to change your outlook on life more than the math-free popularizations or the highly technical math.

I want to have access to outlook-changing insights. So, what math do I need to know? What are the generally applicable mathematical principles that are most worth learning? The above quote seems to indicate at least calculus, and everyone is a fan of Bayesian statistics (which I know little about).

Secondarily, what are some of the most important of that "drop-dead basic fundamental embarrassingly simple mathematics" from different fields? What fields are mathematically based, other than physics and evolutionary biology, and economics?

What is the most important math for an educated person to be familiar with?

As someone who took an honors calculus class in high school, liked it, and did alright in the class, but who has probably forgotten most of it by now and needs to relearn it, how should I go about learning that math?

Eliezer said in the simple math of everything:

It seems to me that there's a substantial advantage in knowing the drop-dead basic fundamental embarrassingly simple mathematics in as many different subjects as you can manage. Not, necessarily, the high-falutin' complicated damn math that appears in the latest journal articles. Not unless you plan to become a professional in the field. But for people who can read calculus, and sometimes just plain algebra, the drop-dead basic mathematics of a field may not take that long to learn. And it's likely to change your outlook on life more than the math-free popularizations or the highly technical math.

What is the most important math for an educated person to be familiar with?

I think that the claim that any prediction can be interpreted in this minimal and consistent framework without exceptions whatsoever is a rather strong claim, I don't think I want to claim much more than that (although I do want to add that if we have such a unique framework that is both minimal and complete when it comes to making predictions then that seems like a very natural choice for Statistics with a capital s).

I don't think we're going to agree about the importance of computability without more context. I agree that every time I try to build myself a nice Bayesian algorithm I run into the problem of uncomputability, but personally I consider Bayesian statistics to be more of a method of evaluating algorithms than a method for creating them (although Bayesian statistics is by no means limited to this!).

As for your other questions: important to note is that your issues are issues with Bayesian statistics as much as they are issues with any other form of prediction making. To pick a frequentist algorithm is to pick a prior with a set of hypotheses, i.e. to make Bayes' Theorem computable and provide the unknowns on the r.h.s. above (as mentioned earlier you can in theory extract the prior and set of hypotheses from an algorithm by considering which outcome your algorithm would give when it saw a certain set of data, and then inverting Bayes' Theorem to find the unknowns. At least, I think this is possible (it worked so far)). And indeed picking the prior and set of hypotheses is not an easy task - this is precisely what leads to different competing algorithms in the field of statistics.

To pick a frequentist algorithm is to pick a prior with a set of hypotheses, i.e. to make Bayes' Theorem computable and provide the unknowns on the r.h.s. above (as mentioned earlier you can in theory extract the prior and set of hypotheses from an algorithm by considering which outcome your algorithm would give when it saw a certain set of data, and then inverting Bayes' Theorem to find the unknowns.

Okay, this is the last thing I'll say here until/unless you engage with the Robins and Wasserman post that IlyaShpitser and I have been suggesting you look... (read more)

0Lumifer12y

The Bayes Rule by itself is not a framework. It's just a particular statistical operation, useful no doubt, but hardly arising to the level of framework. The claim that you can interpret any prediction as forecasting a particular probability distribution has nothing to do with Bayes. For example, let's say that an analyst predicts the average growth in the GDP of China for the next five years to be 5%. If we dig and poke we can re-express this as a forecast of something like a normal distribution centered at 5% and with some width which corresponds to the expected error -- so there is your forecast probability distribution. But is there a particular prior here? Any specific pieces of evidence on which the analyst updated the prior? Um, not really.

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What math is essential to the art of rationality?

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What math is essential to the art of rationality?

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