Things I've been meaning to write:
Mar. 4th, 2006 04:42 pmGoedel, Escher, Bach really is excellent. It's a pretty long book, and the logic is not trivial even for me (I just received my Master's in Logic). He talks about Henkin algebras, the Feferman hierarchy, etc... all the while trying to stay close to his goal of making metaphors with cognition and consciousness.
--
I want to write a methodology book titled "A Bayesian's Guide to Guesstimation". The inspiration was when, yesterday, I tried to combine several pieces of information in order to estimate the number of people were on this huge party complex near Ostbahnhof last night. I had:
* seen crowds of young people walking towards
* I roughly knew the size of the complex
* I roughly know Munich's population, and I had heard of this place twice from "random" strangers.
I could have stood and counted the people coming out of the station... and, using common sense priors about what time people tend go out, etc., I could have made a better guesstimate.
How can we combine common sense knowledge, intuitions, etc., before we have a formal model of the relationship between all the different pieces of information that one has? This is probably very relevant to the study of expert decision-making, i.e. "intelligence analysis".
--
some notes on double-counting proofs:
Double-counting proofs have the form:
x 'means' a, y 'means' a.
Therefore, extension of x = extension of y.
For example,
The number of unit squares in an n x n square is n^2 ("n^2 *means*
the number of unit squares")
The number of unit squares in an n x n square is SUM_{i=0 to n}(2i +
1) (L-construction) ("this SUM *means* the number of unit squares")
Therefore, SUM_{i=0 to n}(2i + 1) = n^2
Examples of theorems provable by double-counting:
* 1 + 3 + ... + (2n + 1) = n^2 (the above theorem)
* binomial theorem
* several properties of Pascal's triangle
I would like to formalize such proofs, so that they become less
confusing, and easy to check (both by humans and by machines). What
kind of formal system do we need in order to represent the semantics
used in such proofs?
I want to make a language with a compositional semantics for talking
about combinatorial structures and combinatorial problems.
;;number of ways of ordering a set of k individuals
(defun fact (n)
(if (= n 0) 1
(* n (fact (- n 1)))))
;;number of ways of picking k individuals from a set of n, where order matters
(defun perm (n k)
(/ (fact n) (fact k)))
;;number of ways of picking k individuals from a set of n, order does not matter
(defun comb (n k)
(/ (perm n) (fact (- n k))))
(a) How many couples are possible when there are M males and F females?
(* M F)
(b) How many *maximal* configurations are possible?, (i.e. either all
males have a partner or all females have a partner)
if M < F, then (perm F M) (i.e. we first sort the males, then pick
M females from a set of F, order being important)
(perm n k) can be seen in many ways. It can be:
* the number of ways of taking k individuals out of a set of n with
order being important,.
* the number of ways of making a line of k individuals from a set of n
(the bijection with the above can be easily visualized: just imagine
the individuals forming a line, each one going to the back of the line
as he gets picked).
* the number of maximal couple matchings
Despite the existence of the bijection between the first two, it seems
to me that, intensionally, these are two differents *meanings*. They
denote the same extensional quantity, but this is not known a priori.
I want a compositional language to talk about extensions, but also a
more expressive compositional language for talking about these
intensions.
While we tend to see men and women as similar types, we probably tend
to think about HAVE relations differently: if you phrase the
combinatorial problem as being about matching men with ties, students
will probably think of it differently. Another inherent bias is that
whereas two ties may be thought of as identical, one would be less
likely to think of two women as identical.
An interesting area of cognitive/conceptual research would be to see
how these "idealizations" are made: how do we figure out the intended
mathematical interpretation of a given word problem?
(c) How many configurations are possible?
one solution is to sum for each number lower than the maximal.
For nCouples = 1, the problem reduces to (a).
For nCouples = M, the problem reduces to (b).
(defun poss-config (perm F M nCouples)
if M < F,
M * F + (M - 1) * (F - 1) + ... + (M - nCouples + 1) * (F - nCouples + 1)
I think Mateja Jamnik has done work on this subject.
--
I want to write a methodology book titled "A Bayesian's Guide to Guesstimation". The inspiration was when, yesterday, I tried to combine several pieces of information in order to estimate the number of people were on this huge party complex near Ostbahnhof last night. I had:
* seen crowds of young people walking towards
* I roughly knew the size of the complex
* I roughly know Munich's population, and I had heard of this place twice from "random" strangers.
I could have stood and counted the people coming out of the station... and, using common sense priors about what time people tend go out, etc., I could have made a better guesstimate.
How can we combine common sense knowledge, intuitions, etc., before we have a formal model of the relationship between all the different pieces of information that one has? This is probably very relevant to the study of expert decision-making, i.e. "intelligence analysis".
--
some notes on double-counting proofs:
Double-counting proofs have the form:
x 'means' a, y 'means' a.
Therefore, extension of x = extension of y.
For example,
The number of unit squares in an n x n square is n^2 ("n^2 *means*
the number of unit squares")
The number of unit squares in an n x n square is SUM_{i=0 to n}(2i +
1) (L-construction) ("this SUM *means* the number of unit squares")
Therefore, SUM_{i=0 to n}(2i + 1) = n^2
Examples of theorems provable by double-counting:
* 1 + 3 + ... + (2n + 1) = n^2 (the above theorem)
* binomial theorem
* several properties of Pascal's triangle
I would like to formalize such proofs, so that they become less
confusing, and easy to check (both by humans and by machines). What
kind of formal system do we need in order to represent the semantics
used in such proofs?
I want to make a language with a compositional semantics for talking
about combinatorial structures and combinatorial problems.
;;number of ways of ordering a set of k individuals
(defun fact (n)
(if (= n 0) 1
(* n (fact (- n 1)))))
;;number of ways of picking k individuals from a set of n, where order matters
(defun perm (n k)
(/ (fact n) (fact k)))
;;number of ways of picking k individuals from a set of n, order does not matter
(defun comb (n k)
(/ (perm n) (fact (- n k))))
(a) How many couples are possible when there are M males and F females?
(* M F)
(b) How many *maximal* configurations are possible?, (i.e. either all
males have a partner or all females have a partner)
if M < F, then (perm F M) (i.e. we first sort the males, then pick
M females from a set of F, order being important)
(perm n k) can be seen in many ways. It can be:
* the number of ways of taking k individuals out of a set of n with
order being important,.
* the number of ways of making a line of k individuals from a set of n
(the bijection with the above can be easily visualized: just imagine
the individuals forming a line, each one going to the back of the line
as he gets picked).
* the number of maximal couple matchings
Despite the existence of the bijection between the first two, it seems
to me that, intensionally, these are two differents *meanings*. They
denote the same extensional quantity, but this is not known a priori.
I want a compositional language to talk about extensions, but also a
more expressive compositional language for talking about these
intensions.
While we tend to see men and women as similar types, we probably tend
to think about HAVE relations differently: if you phrase the
combinatorial problem as being about matching men with ties, students
will probably think of it differently. Another inherent bias is that
whereas two ties may be thought of as identical, one would be less
likely to think of two women as identical.
An interesting area of cognitive/conceptual research would be to see
how these "idealizations" are made: how do we figure out the intended
mathematical interpretation of a given word problem?
(c) How many configurations are possible?
one solution is to sum for each number lower than the maximal.
For nCouples = 1, the problem reduces to (a).
For nCouples = M, the problem reduces to (b).
(defun poss-config (perm F M nCouples)
if M < F,
M * F + (M - 1) * (F - 1) + ... + (M - nCouples + 1) * (F - nCouples + 1)
I think Mateja Jamnik has done work on this subject.
(no subject)
Date: 2006-03-04 06:49 pm (UTC)To formalize the proof that even the union of the L-shapes makes a square seems like a lot more work than the instantaneous recognition that we do visually.
(no subject)
Date: 2006-03-04 07:04 pm (UTC)Great! You've already come up with some axioms. :-)
If what people do when they do these reasonings is serial (no weird parallel neural processing going on at this level), then I believe it should be "easy" to formalize these reasonings. This is my general position on formalizations.
My general philosophical position is:
* Scientists have logical thoughts.
* It would be a most excellent thing to have an electronic library of the formal correspondants of these thoughts (assumptions, concepts, arguments), i.e. axioms, definitions, theorems.
You could say that I wish to formalize all academic discourse (or to begin with, all "well-known" stuff: undergraduate curricula, etc). I see no reason why this would be impossible. In fact, I think the amount of work involved is on the order of magnitude of the CYC project.
(no subject)
Date: 2006-03-04 07:12 pm (UTC)(no subject)
Date: 2006-03-04 07:28 pm (UTC)Which brings up the fact that we do in fact consider symbols "up to isomorphism" in more cases than we realize; an x set in a smaller font in the superscript of some symbol is the same x, and it's not even (if you're using TeX) exactly the same image as the bigger x scaled down, for TeX does optical font scaling, changing the proportions of letters at various font sizes to make them more legible.
So at the very low level, I think there is very little hope of formalizing what's going on, because it depends too finely on what our brains are actually doing — but this hasn't hindered formalization of symbolic proofs, nor should it hinder formalization of diagrammatic proofs. For we succeeded in finding various good sets of middle-level primitives, (for instance, first-order logic, as you mentioned in your other reply) and I suppose then that there is hope for finding good sets of diagrammatic primitives. Maybe Jamnik already has — I should go read her stuff.
(no subject)
Date: 2006-03-04 07:39 pm (UTC)which reminds me of this previous post of mine, in which I quoted
Manfred Kerber, Martin Polleti - On the Design of Mathematical Concepts
i.e. people in the ATP community are using their hammer on too many things, because in fact it works to some extent. But not everything is a nail... which is why using representations that mirror the concepts (and logics?) used by human mathematicians (e.g. visual concepts) can improve ATP at its core.
--
But actually, I don't see why diagrammatic reasoning would be inherently more "heuristic" than sentential reasoning.
(no subject)
Date: 2006-03-04 07:57 pm (UTC)I guess my point is that if you can put your reasoning into a few words, or think about it in a conscious / "serial" way, that means that this formalism is already implemented inside the very restricted machine in which conscious thought happens. As a consequence, these "middle-level primitives" can also be implemented on a computer. My main premise is the simplicity of human consciousness.
But from the standpoint of a general intelligence, what you call "middle-level primitives" are actually very low-level (low Kolmogorov Complexity). For a human mind, logic may in fact be high level, but this is only because the brain is a terribly inefficient machine in which logic is implemented on top of neural networks (imagine that!!).
(no subject)
Date: 2006-03-04 07:57 pm (UTC)believing is seeing
Date: 2006-03-04 08:27 pm (UTC)We only sense this "mathematical sense datum" because we believe in induction.