In search of uncertainty

Back when I was in school, I was a math stud. At least people around me thought so. I knew I wanted to pursue a career in science, and that in part led me to taking science in class XI, and subsequently writing JEE which led to the path I ultimately took. Around the same time (when I was in high school), I started playing chess competitively. I was quite good at it, and I knew that with more effort I could make it big in the game. But then, that never happened, and given that I would fall sick after every tournament, I retired.

It was in 2002, I think, that I was introduced to contract bridge, and I took an instant liking for it. All the strategising and brainwork of chess would be involved once again, and I knew I’d get pretty good at this game, too. But there was one fundamental difference which made bridge so much more exciting – the starting position was randomized (I’m not making a case for Fischer Chess here, mind you). The randomization of starting positions meant that you could play an innumerable number of “hands” with the same set of people without ever getting bored. I simply loved it.

It was around that time that I started losing interest in math and other hard sciences. They had gotten to the point where they were too obscure, and boring, I thought, and that to make an impact in them, I wanted to move towards something less precise, and hard. That was probably what led me to do an MBA. And during the course of my MBA I discovered my interest in economics and social sciences, but am yet to do anything significant on that front, though, apart from the odd blog here or there.

I think what drove me from what I had thought is my topic of interest to what I think now it is is the nature of open problems. In hard sciences, where a lot of things are “known” it’s getting really hard to do anything of substance unless you get really deep in, into the territories of obscurity. In “fuzzy sciences”, on the other hand, nothing too much is “known”, and there will always be scope for doing good interesting work without it getting too obscure.

Similarly, finance, I thought, being a people-driven subject (the price of a stock is what a large set of people think its price is, there are no better models) will have lots of uncertainty, and scope to make assumptions, and thus scope to do good work without getting too obscure. But what I find is that given the influx of hard science grads in Wall Street over the last three decades, most of the large organizations are filled with people who simply choose to ignore the uncertainty and “interestingness” and instead try and solve deterministic problems based on models that they think completely represents the market.

And this has resulted in you having to do stuff that is really obscure and deep (like in the hard sciences) even in a non-deterministic field such as finance, simply because it’s populated by people from hard science background, and it takes way too much in order to go against the grain.

PS: Nice article by Tim Harford on why we can’t have any Da Vincis today. Broadly related to this, mostly on scientific research.

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