One of the central ideas motivating my research is expressed by Herb Simon in the following quote:

Q: So you have moved from field to field as you could bring new tools to bear on your study of decision making?

A: I started off thinking that maybe the social sciences ought to have the kinds of mathematics that the natural sciences had. That works a little bit in economics because they talk about costs, prices and quantities of goods. But it doesn't work a darn for the other social sciences; you lose most of the content when you translate them to numbers.

So when the computer came along -- and more particularly, when I understood that a computer is not a number cruncher, but a general system for dealing with patterns of any type -- I realized that you could formulate theories about human and social phenomena in language and pictures and whatever you wanted on the computer and you didn't have to go through this straitjacket of adding a lot of numbers.

As Dijkstra said, Computer Science is not about computers. It is about processes.

It is a very common error is for people to make an argument like the following:

Stock prices have to do with human behavior. Therefore they are unpredictable. It's not like physics, where computers and mathematical models are useful.

I go all "oy vey" whenever I hear arguments like this... and then, they accuse me of reductionism.

My mom doesn't like it when I interview doctors trying to formalize their knowledge about my problem, so I can truly understand my problems. At the same time, she says (non-sarcastically) I should go into biomedical research.

The other day, I wrote the following on my PDA:

Why I am no longer a mathematician:
· Tired of working hard just to be clever. Life is short. The real world is more interesting.
· Phenomenology, introspection drove me towards cogsci.
· it's more productive to do meta work: computers will eventually do math much more cheaply than me. (see Zeilberger)


----


Here's something of an academic autobiography, of my time at Bucknell. It says nothing about my ideas, or what I read. I tell the story of how undergraduate curricula shaped my choice of majors:


Mathematics

The last time I did serious mathematical research was my junior year of college... and even that was very much empirically-aided: it was about counting the number of roots of polynomials over finite fields... my discoveries were made with the aid of a C++ compiler.
Since then, I have proven things about cute games (Nim, thanks to agnosticessence), toy theorems (prove that number_of_divisors_of(n) is always even except for when n is a perfect square), and created neat correspondences (e.g. if you represent natural numbers as multisets, GCD is intersection, LCM is union), but nothing that could count as serious mathematics.

Already my senior year, in topology class, I no longer saw the point of doing pure math. The only way I could interpret infinite products of topological spaces was as a game of symbols: it had no real meaning to me.

Not only was I starting to get a formalistic view of mathematics, but I was increasingly bothered by the normal approach to mathematics, the standard mathematical language and the paper medium. This was made much worse by the fact that I had grown intolerant of confusing notation/language and informal proofs. Thankfully, I didn't stay in mathematics. Advanced mathematics requires a lot of effort and things are not always beautiful. The real world has many more interesting things to understand. During this time, I considered going for a PhD in Applied Math, but became disappointed with that idea too. It was still too much like other math.

By my senior year, mathematics was no longer fun. Still not "hard", but I no had motivation left. I had become enthusiastic about statistical modelling... even if I got labelled a Bayesian by our frequentistics department (I think it was meant as a compliment). And it was my interest in AI, by far, that dominated my intellect.


Physics

The reason I had liked mathematics before that was that it had been, for me, easy and fun. And its formal structures were much more satisfactory and easier for me to understand than the things people did in physics, my original major. My physics teachers never seemed to explain things clearly, and never gave me good logical reasons for why they were doing what they were doing. It was often unclear which model and assumptions were being used. And even after pressing them, I still had foundational questions that went unanswered. Quantum Mechanics class was extremely frustrating: while "nobody understands quantum mechanics", the theory still has a reason to be, but they didn't give us a chance to try to make sense of the experimental results that motivated the theory, or convince me that the theory was the best we could do.

Although I started out with bad grades in physics, they were steadily improving. Still, my professors saw promise in me, and wanted me to stay. Despite liking and doing well on my last class on Thermodynamics & Statistical Mechanics, I decided that I was going to focus on math: I was just too different from the physicists, and talking to them took too much effort. Now I want Patrick Suppes to be my next physics teacher. Among the physicists, I was definitely a philosopher.


Computer Science

I had to overcome my initial prejudice against CS. I only started it because of my father's argument that it would be a good idea if I wanted to make money. As a freshman, I had thought that it was just going to be about programming techniques, and similar boring-sounding things. The sort of person who did CS at my school was not far from the "typical management major": financially ambitious, if not particularly mathematically-talented. When I joined the group, I learned that there were exceptions... so now, I realized that there were also "computer geeks", as well as the former type. I was never a "computer geek". Programming geek, yes, for a long time... but one who couldn't get Linux installed, and who would call a technician to troubleshoot my network. Among them, I was solidly seen as a math geek. It bothered me that their AI class assumed neither knowledge of basic probability or basic logic, and that the computer graphics class couldn't do a simple linear projection.
But I really liked ProgLan. Also, designing algorithms was fun. Algorithmic reductions even more. And I learned some useful programming techniques.


Philosophy

I've always been a philosopher. But I did not like the prospect of reading shelffuls of philosophy books, learning the ins and outs of useless arguments (for instance, about metaphysics), and rereading & struggling to understand what exactly writers mean. Philosophy is great for breaking people out of their epistemological vices: questioning their prejudices, intuitions, etc., but some things are just overanalyzed. I think this is because they talk past each other. Case in point: the Monty Hall problem. Why are they still writing papers about it?? I think that philosophers should benefit the most from computational aids to reasoning, argumentation maps and such. At least, they already know logic.


Psychology

It was fascinating. But it wasn't rigorous enough for me. If they had offered cognitive science, I probably would have taken lots of it.


Economics & Linguistics

I also flirted with economics, although never for credit. It was interesting, but they were too slow on the math. Like CS, only worse. I also took a class in linguistics (the only one offered!), but as I wasn't about to start doing NLP, it remained a mere curiosity.