Dan Hirschman, grad student at the University of Michigan, has a great post on rational choice theory. It is framed as a critique, but I consider it wholly compatible with my defense of RCT. Human behavior is complex and different aspects of it will be best understood with different theories/models/levels of analysis. See some of my previous posts on modeling here.
Graduate students who are interested in large-scale networks should read this post by Tom Lento, who is a data scientist at Facebook (and a Cornell sociology grad), about Facebook’s new graduate fellowship program.
It’s pretty frustrating that, given the quasi-social science Facebook is doing, the listing does not explicitly mention social science disciplines, but I have gotten word that social scientists with appropriately strong computational skills (i.e. candidate should be a reasonably competent programmer) can be considered.
That said, it’s great that Facebook is willing to work with academics, especially graduate students, to do interesting things that might have some general scientific benefit (in addition to direct product benefit). This puts them in the category of Microsoft, IBM, HP, Google, Yahoo, and other such tech companies, who also fund sizable graduate student internship programs.
Facebook has a treasure trove of micro-level interaction data. If you want to work with that data, this might be the best way to do it.
Most quantitative social scientists, myself included, master particular statistical techniques, but have limited understanding of the breadth or history of statistical practice. Academic specialization is necessary, but sometimes we could learn a lot by taking a broader view. I found it interesting to learn a little more about some of the most influential statisticians and their contributions in this article by Daniel Wright: “Ten Statisticians and their Impacts for Psychologists.”
Though I enjoyed Wright’s piece, one thing I felt was missing was a connection to philosophy and sociology of science. What are the goals of empirical research in the social sciences? How have the methods these statisticians invented changed social science, and science more generally?
Wright, D. (2009). Ten Statisticians and Their Impacts for Psychologists Perspectives on Psychological Science, 4 (6), 587-597 DOI: 10.1111/j.1745-6924.2009.01167.x
Yeah so, publishing in Nature is something i’d like to knock off the list at some point (i thought we had it nabbed a while back, but that particular paper now seems stuck in permanent limbo, but i digress); unfortunately it hasn’t happened thus far. If i could accomplish that goal with a piece like this one, i think i would be doubly excited. The title for the letter is “Fetal load and the evolution of lumbar lordosis in bipedal hominins,” which is roughly translated by the Ig Nobel Prize* announcement it won (see here, scroll to Physics Prize) as “analytically determining why pregnant women don’t tip over.”
* described as being “For achievements that first make people LAUGH then make them THINK.”
Arnold Kling was asked:
With regard to the recent financial crisis and current economic recession – if you were given the power to go back in time and change only one thing in an effort to prevent the crisis and recession, what year would you choose, and what one thing would you change?
He answers here. You also might want to check out his new book with Nick Schulz called Poverty to Prosperity: intanigible Assets, Hidden Liabilities and The Lasting Triumph over Scarcity. Much of it consists of interviews with highly noted economists. I’ve only had the chance to read a few chapters so far but I’ll definitely look at it again when I have a little more time.
This past weekend I found myself listening to This American Life, a quirky show that tells a variety of stories about the American experience. The most recent show included a discussion of the potential and pitfalls in economic forecasting. And, as it turns out, predictive models of the national economy aren’t very good- with margins of error wide enough to straddle the range from “sluggish with rising unemployment” to “robust with decreasing unemployment.” That’s a little bit like going to your doctor and being told that, given your test results, you’re either going to life for another thirty years or be dead in six months. Most of us would probably not find such a prognosis terribly useful. Yet, what emerged on the show was not only an acknowledgement that economic forecasting is chancy at best- there was some discussion actually that predictions should be made along the lines of “growth will be two-ish percent”- but a wry commentary on the degree of precision in those estimates. Indeed, while the margin of error is so wide as to encompass both boom and bust, the predictions themselves regularly include two or more decimal points. It is as though the doctor has said that you will live for either thirty years and one hundred days, or six months and eight hours. By the time the range is that large, including the extra bits seems a tad silly. Yet, pointless or no, the precision is in the estimates and, more to the point, is actually demanded by the consumers. Even though the people who use these forecasts are aware of how inaccurate they can be they nevertheless seem to want all those extraneous decimal points.
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