andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-1076 knowledge-graph by maker-knowledge-mining
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Introduction: Interesting thoughts from Kaiser Fung. Derman seems to have a point in his criticisms of economic models—and things are just as bad in other social sciences. (I’ve criticized economists and political scientists for taking a crude, 80-year-old model of psychology as “foundational,” but even more sophisticated models in psychology and sociology have a lot of holes, if you go outside of certain clearly bounded areas such as psychometrics.) What can be done, then? One approach, which appeals to me as a statistician, is to more carefully define one’s range of inquiry. Even if we don’t have a great model of political bargaining, we can still use ideal-point models to capture a lot of the variation in legislative voting. And, in my blog post linked to above, I recommended that economists forget about coming up with the grand unified theory of human behavior (pretty impossible, given that they still don’t want to let go of much of their folk-psychology models) and put more effort i
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1 (I’ve criticized economists and political scientists for taking a crude, 80-year-old model of psychology as “foundational,” but even more sophisticated models in psychology and sociology have a lot of holes, if you go outside of certain clearly bounded areas such as psychometrics. [sent-3, score-0.852]
2 One approach, which appeals to me as a statistician, is to more carefully define one’s range of inquiry. [sent-5, score-0.125]
3 Even if we don’t have a great model of political bargaining, we can still use ideal-point models to capture a lot of the variation in legislative voting. [sent-6, score-0.443]
4 At the same time, governments, businesses, and other organizations need to make decisions involving macroeconomics. [sent-8, score-0.089]
5 And, in my own applied statistical work, I use imperfect models all the time. [sent-10, score-0.342]
6 Here’s what Kaiser writes: The insurmountable challenge of social science models, which constrains their effectiveness, is that the real drivers of human behavior are not measurable. [sent-11, score-0.735]
7 What causes people to purchase goods, or vote for a particular candidate, or become obese, or trade stocks is some combination of desire, impulse, guilt, greed, gullibility, inattention, curiosity, etc. [sent-12, score-0.406]
8 What modelers can measure are things like age, income, education, past purchases, objects owned, etc. [sent-14, score-0.326]
9 Nowadays, we can log every keystroke you type on your smartphone. [sent-15, score-0.082]
10 That models are even half-accurate is due to the correlation of these measured quantities with the hidden drivers of our behavior but this correlation is only partial. [sent-16, score-1.203]
11 Now add to that, the vagaries of human behavior. [sent-17, score-0.168]
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same-blog 1 1.0 1076 andrew gelman stats-2011-12-21-Derman, Rodrik and the nature of statistical models
Introduction: Interesting thoughts from Kaiser Fung. Derman seems to have a point in his criticisms of economic models—and things are just as bad in other social sciences. (I’ve criticized economists and political scientists for taking a crude, 80-year-old model of psychology as “foundational,” but even more sophisticated models in psychology and sociology have a lot of holes, if you go outside of certain clearly bounded areas such as psychometrics.) What can be done, then? One approach, which appeals to me as a statistician, is to more carefully define one’s range of inquiry. Even if we don’t have a great model of political bargaining, we can still use ideal-point models to capture a lot of the variation in legislative voting. And, in my blog post linked to above, I recommended that economists forget about coming up with the grand unified theory of human behavior (pretty impossible, given that they still don’t want to let go of much of their folk-psychology models) and put more effort i
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Introduction: Econometrician and statistician Dale Poirier writes: 24 years ago (1988, Journal of Economics Perspectives) I [Poirier] noted cognitive dissonance among some economists who treat the agents in their theoretical framework as Bayesians, but then analyze the data (even in the same paper!) as a frequentist. Recently, I have found similar cases in cognitive science. I suspect other disciplines exhibit such behavior. Do you know of any examples in political science? My reply: I don’t know of any such examples in political science. Game theoretic models are popular in poli sci, but I haven’t seen much in the way of models of Bayesian decision making. Here are two references (not in political science) that might be helpful. 1. I have argued that the utility model (popular in economics and political science as a way of providing “microfoundations” for analyses of aggregate behavior) is actually more of a bit of folk-psychology that should not be taken seriously. To me, it is si
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Introduction: Michael Collins sent along the following announcement for a talk: Fast learning algorithms for discovering the hidden structure in data Daniel Hsu, Microsoft Research 11am, Wednesday April 10th, Interschool lab, 7th floor CEPSR, Columbia University A major challenge in machine learning is to reliably and automatically discover hidden structure in data with minimal human intervention. For instance, one may be interested in understanding the stratification of a population into subgroups, the thematic make-up of a collection of documents, or the dynamical process governing a complex time series. Many of the core statistical estimation problems for these applications are, in general, provably intractable for both computational and statistical reasons; and therefore progress is made by shifting the focus to realistic instances that rule out the intractable cases. In this talk, I’ll describe a general computational approach for correctly estimating a wide class of statistical mod
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Introduction: Cosma Shalizi and Larry Wasserman discuss some papers from a conference on Ockham’s Razor. I don’t have anything new to add on this so let me link to past blog entries on the topic and repost the following from 2004 : A lot has been written in statistics about “parsimony”—that is, the desire to explain phenomena using fewer parameters–but I’ve never seen any good general justification for parsimony. (I don’t count “Occam’s Razor,” or “Ockham’s Razor,” or whatever, as a justification. You gotta do better than digging up a 700-year-old quote.) Maybe it’s because I work in social science, but my feeling is: if you can approximate reality with just a few parameters, fine. If you can use more parameters to fold in more information, that’s even better. In practice, I often use simple models—because they are less effort to fit and, especially, to understand. But I don’t kid myself that they’re better than more complicated efforts! My favorite quote on this comes from Rad
Introduction: Nick Brown is bothered by this article , “An unscented Kalman filter approach to the estimation of nonlinear dynamical systems models,” by Sy-Miin Chow, Emilio Ferrer, and John Nesselroade. The introduction of the article cites a bunch of articles in serious psych/statistics journals. The question is, are such advanced statistical techniques really needed, or even legitimate, with the kind of very rough data that is usually available in psych applications? Or is it just fishing in the hope of discovering patterns that are not really there? I wrote: It seems like a pretty innocuous literature review. I agree that many of the applications are silly (for example, they cite the work of the notorious John Gottman in fitting a predator-prey model to spousal relations (!)), but overall they just seem to be presenting very standard ideas for the mathematical-psychology audience. It’s not clear whether advanced techniques are always appropriate here, but they come in through a natura
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Introduction: Interesting thoughts from Kaiser Fung. Derman seems to have a point in his criticisms of economic models—and things are just as bad in other social sciences. (I’ve criticized economists and political scientists for taking a crude, 80-year-old model of psychology as “foundational,” but even more sophisticated models in psychology and sociology have a lot of holes, if you go outside of certain clearly bounded areas such as psychometrics.) What can be done, then? One approach, which appeals to me as a statistician, is to more carefully define one’s range of inquiry. Even if we don’t have a great model of political bargaining, we can still use ideal-point models to capture a lot of the variation in legislative voting. And, in my blog post linked to above, I recommended that economists forget about coming up with the grand unified theory of human behavior (pretty impossible, given that they still don’t want to let go of much of their folk-psychology models) and put more effort i
Introduction: Rajiv Sethi writes the above in a discussion of a misunderstanding of the economics of Keynes. The discussion is interesting. According to Sethi, Keynes wrote that, in a depression, nominal wages might be sticky but in any case a decline in wages would not do the trick to increase hiring. But many modern economics writers have missed this. For example, Gary Becker writes, “Keynes and many earlier economists emphasized that unemployment rises during recessions because nominal wage rates tend to be inflexible in the downward direction. . . . A fall in price stimulates demand and reduces supply until they are brought back to rough equality.” Whether Becker is empirically correct is another story, but in any case he is misinterpreting Keynes. But the actual reason I’m posting here is in reaction to Sethi’s remark quoted in the title above, in which he endorses a 1975 paper by James Tobin on wages and employment but remarks that Tobin’s paper did not include the individual-level de
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Introduction: Following up on our recent discussion of the problems of considering utility theory as a foundation for economic analysis (which in turn was a reprise of this post from last September), somebody named Mark pointed me to a 2007 article by Luigino Bruni and Robert Sugden, “The road not taken: How psychology was removed from economics, and how it might be brought back,” which begins: This article explores parallels between the debate prompted by Pareto’s reformulation of choice theory at the beginning of the twentieth century and current controversies about the status of behavioural economics. Before Pareto’s reformulation, neoclassical economics was based on theoretical and experimental psychology, as behavioural economics now is. Current discovered preference defences of rational-choice theory echo arguments made by Pareto. Both treat economics as a separate science of rational choice, independent of psychology. Both confront two fundamental problems: to find a defensible defi
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