False dichotomies (and synonymies)

It’s been a while. Let me allow you a few moments to catch your breath over the surprise of me posting (a real post) here again.

…[twiddles thumbs] [taps foot] [checks watch]…

OK, feel better? On with it.

Back in the middle-stages of grad school, i started to hear a lot of harumphing* about the frustrations of qualitative folks and all of the “quant-shop” requirements of our particular program. At the same time, i couldn’t help but notice the dismissiveness of some of the most quantitatively oriented folks towards brilliant qualitative work we’d occasionally discuss. Now, i know that the quant-qual divide was not unique to our program, is not something new to sociology, and has been pointed out as a false dichotomy by many (more qualified) folks who’ve passed through these ranks before.

But what strikes me today is that for some reason, in my limited experience at least, most folks also assume a necessary overlap between “mathematical” sociology and “quantitative” sociology…

Do we want to be unified?

Teppo Felin already blogged this at Org Theory but I thought I’d raise one question about Herb Gintis’s proposal for the unification of the behavioral sciences (paper and lecture).  My question is this: would unification of the behavioral sciences discourage methodological and theoretical innovation?

As an interdisciplinary scholar, I am often frustrated but my fellow social scientists lack of regard for the insights gained in sister disciplines. Unification would seem to fix that problem, but some might argue that more unified academic standards would discourage  innovation.  The idea is that each discipline is currently like a separate experiment, and unifying them would be putting all our eggs in one basket.

I’d be interested to hear what other people think about this argument, but I’m inclined to believe we can pursue unification and intellectual diversity at the same time.  (post edited for clarity)

Setting an Agenda for the Social Sciences?

This sounds like a great conference! Though the distinguished participants have a great deal of wisdom, and I fully support attempts to tackle big questions, the summary reminds me of the limits of this approach.  How accurately do you think we can predict where the big advances will come in the next fifty years?  Determining which advances in the past fifty years were most important would seem to be much easier, but there would be a lot of disagreement on that question too, even if you restricted the sample to sociologists.  Shouldn’t we be a bit disturbed by this?

If you want to take a shot at these questions I’d start by specifying the value of different types of advances, and then outline which type of advances are most likely.

Does P=NP?

If P=NP, then the world would be a profoundly different place than we usually assume it to be. There would be no special value in “creative leaps,” no fundamental gap between solving a problem and recognizing the solution once it’s found. Everyone who could appreciate a symphony would be Mozart; everyone who could follow a step-by-step argument would be Gauss; everyone who could recognize a good investment strategy would be Warren Buffett. It’s possible to put the point in Darwinian terms: if this is the sort of universe we inhabited, why wouldn’t we already have evolved to take advantage of it?  – Scott Aaronson (reason #9)

When you have some free time, watch this amazing lecture by Avi Wigderson about one of the great open problems in all of mathematics.

Mistaking Beauty for Truth

Peter Klein agrees with Paul Krugman that economists have mistaken beauty for truth, but disagrees that it has anything to do with the financial crisis so he won’t be signing the Hodgson petition.

Also at the Organizations and Markets blog, Nicolai Foss discusses a special journal issue entitled “Economic Models as Credible Worlds or as Isolating Tools?”

Speaking of models and truth and beauty, I found Murray Gell-Mann’s TED Talk fascinating.  He argues that in physics, a beautiful theory is more likely to be true.  This makes me a little nervous.  I would be especially worried about using this heuristic in the social sciences because the objects we are studying are complex, and have so much meaning to us that our aesthetic sense is more likely to attach to theories for reasons other than truth.  Truth may be beautiful, but so are our cognitive biases and ideologies.

Forced to Defend Rational Choice Theory

I’m finally responding to Eli Thorkelson, who asked for my comments on an article by feminist economist Julie Nelson.  The article is a critique of rational choice theory (RCT) and I think it has some omissions and misleading claims.

I regularly come across particular instances of rational choice theorizing that I dislike, but non-economists, including sociologists, often dismiss rational choice theory without understanding it, so when the topic comes up among non-economists, I almost inevitably find myself defending it.  My claim is that rational choice theory, broadly construed, is an important, though certainly not the only, useful framework for understanding human behavior.  This should be considered an utterly boring claim.  What is interesting is how any social scientist could deny it…

Mathematical models: why and what for?

An anthropologist friend of mine, Eli Thorkelson, recently asked for my comments on an article by feminist economist Julie Nelson. The article is a critique of rational choice theory (RCT) and I think it has some glaring omissions and misleading claims, but before I get to them I want to briefly offer the positive case for mathematical models, of which rational choice models are a subset.*

Markets and other social phenomena are complex.  We’re never going to have a theory that perfectly describes everything we might want to know about them.  Modeling is one way we can get some insight.  In a model we make some simplifying assumptions and then work out their implications.  Then, and this is key, we do empirical work; we analyze data to see how well our model captures various aspects of the phenomena we are interested in.  Ideally a  model will make predictions that are novel, in the sense that the researcher would not have made them before studying the model, and in the sense that they distinguish one model from another in a way that can be tested rigorously.

What about those simplifying assumptions?  If they turn out to be false, doesn’t that mean the model is junk?