definition of semantics ; Wikipedia on Model_(logic)

I'm kinda proud of my wiki page on semantics (which is really a pretty stubby index to other wiki pages on all kinds of semantics), especially my mathematical definition.

I find it strange that Wikipedia has no article called Model_(logic). It makes no sense for them have an article on model theory if they don't define "model". Also, correct me if I'm wrong, but saying that model theory is about the "representation of mathematical concepts in terms of set theory" is total BS.

As important as it is to study the representation of mathematical concepts, I don't think it's a well-defined area of study/research, and if it were, it should be called "formalization studies" or (imagining a good future) "mathematical knowledge representation". Also, you can construct mathematical objects with whatever foundation you want. Why do so many people have a fetish for set theory?

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UPDATE: Who the hell wrote that the semantics of I-F logic is in terms of "zero-sum games"? These are win-lose games! Sigh... Wikipedia...

Answer: a troll, of course.

Giovanni Sambin's philosophy

Giovanni Sambin is one of the most interesting logicians I know. His "basic logic" seems to be a minimalist idea, embodying only the principle of reflection/variable-substitution, from which he unifies (all?) other logics.

I like this:

One of my principles is that one should study the mind and its products, including mathematics, just as natural scientists study nature.
So with no skyhooks (in the sense of D. Dennett): everything can, and should, be explained virtually from the bottom, that is basing only on the laws of biology.

He calls his philosophy "dynamic constructivism". Reading the paper, it almost sounds like a Buddhist philosophy (all this talk of being attached to Platonism, static foundations, suffering, etc.). I wonder what Henk Barendregt would think.

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On the same theme of unifying/formalizing philosophies of mathematics, Ed Zalta is speaking in Amsterdam next month. One year too late for me.

Models, Simulations, and Experiments

Francesco Guala - Models, Simulations, and Experiments

My thoughts:
* In mathematics, simulations and experiments are the same thing.

* As an AI/Cogsci person, I would like to see a simulation in which resource-bounded agents each make a simulation of their world. Looking at their performance might shed some light on how *we* should make our simulations in the real world (this may be especially true, if you believe that we are living in a simulation). Even if the lesson is too hard for us to understand, e.g. imagine that one of the simulated agents came up with crazy feature selection algorithm, maybe using neural networks (or some other algorithm that is a blackbox to us). We might still benefit from copying their algorithm and using it in the real world... especially if we try to make sure that the reason it works is not because it exploits artifacts of the simulation (one way of doing this is to make sure that the algorithm is robust across different simulations, written by different people).

I'm reminded of this idea:
* Debugging is like the scientific method: you combine theory (reasoning about programs) and experiment (testing). The difference is that debugging is easier:
** computer programs are known to be deterministic, and we can control initial conditions.
** closed world: when debugging, there is a bounded number of things that could be causing the undesired behavior. The evil genie of worst-case can only be so evil.

data: people disagreeing on the Internet

My research project, "learning argumentative structures", has generated lots of nice ideas, but not much in the way of concrete progress. It is time to get some data.

I want to see lots of instances of people disagreeing, and justifying their position (with real arguments: we are not interested in ad-hominem, but fallacious and unsupported arguments are ok), so I can make argument maps of it. I'm looking for blogs, mailing list archives, chat logs, etc in which people have discussions.

Desired properties of the data / medium:
(1) people use quotes, or somehow indicate precisely what sentence they disagree with.
(2) quotes are short, responses are short.
(3) redundancy: the same arguments and rebuttals appear many times using different words (linking, trackback all help here)

Some candidate source types:
* blogs: so-so about (1), so-so about (2). Good about (3), thanks to extensive linking and trackback.
* mailing lists: so-so about (1).
* chats: very good about (1) and (2), bad about (3).

I'm leaning towards making experiments with the chat medium, that somehow induce the subjects to talk about the same arguments. Hopefully, someone has done this for me already.