I was suggesting that what model you should use if your current one is incorrect is based on how you got your current model, which is why it sounds like ‘I prefer real world problems’ - model generation details do seem necessarily specific. (My angle was that in life, few things are impossible, many things are improbable—like getting out of the desert and not paying.) I probably should have stated that, and that only, instead of the math.
by representing the input of the problem explicitly I’ve created an abstraction that is closer to the real world than most of these problems are.
Indeed. I found your post well thought out, and formal, though I do not yet fully understand the jargon.
Thanks, I appreciate the complement. Even though I have a maths degree, I never formally studied decision theory. I’ve only learned about it by reading posts on Less Wrong. So much of the jargon is my attempt to come up with words that succinctly describe the concept.
I was suggesting that what model you should use if your current one is incorrect is based on how you got your current model, which is why it sounds like ‘I prefer real world problems’ - model generation details do seem necessarily specific. (My angle was that in life, few things are impossible, many things are improbable—like getting out of the desert and not paying.) I probably should have stated that, and that only, instead of the math.
Indeed. I found your post well thought out, and formal, though I do not yet fully understand the jargon.
Where/how did you learn decision theory?
Thanks, I appreciate the complement. Even though I have a maths degree, I never formally studied decision theory. I’ve only learned about it by reading posts on Less Wrong. So much of the jargon is my attempt to come up with words that succinctly describe the concept.