Physics Envy

The NYT published an op-ed today by a pair of political scientists on "physics envy" by sociologists, economists, and political scientists. The authors mainly argue that theory can be useful even when it is wrong or unsupported by data, and briefly mention that data analysis is useful even if theoretical contributions are not obvious. I disagree with the former, but not the latter. For a similar view, see this post by the theoretical physicist Sean Carroll.

Irving Louis Horowitz

The eminent political sociologist died a few days ago, according to an obit in the NYT. Long ago I read, and took seriously, his book The Decomposition of Sociology, in which he argues (essentially) for more empirical analysis and less left-wing politics in sociology. Reflecting on his book, he neglects a fundamental, possible cultural contradiction: to the extent social reality exhibits facts consistent with liberalism and inconsistent with conservatism, empirical analysis will result in more liberal than conservative belief systems (but not values, since those cannot be proven "right" or "wrong" by scientific analysis). For example, evidence is accumulating that economic inequality (which is of little concern to most conservatives in the United States), has numerous deleterious effects, thus forcing conservatives either to hold beliefs inconsistent with the evidence (i.e., inequality is unrelated to deleterious effects) or alter their values (i.e., it is a "good" thing to have high rates of violence, low social mobility, and so forth).

MyPersonality

I highly recommend this website for learning about your attitudes, values, beliefs, and overall personality.

Why are Economists so (Consistently) Led Astray About Inequality?

In a recent Boston Globe article Ed Glaeser, a conservative urban economist at Harvard, wrote an article titled Why income disparity in Boston isn't a bad thing. Glaeser is right that inequality increases in a city such as Boston can be due to selection effects, since poor people are moving into Boston for economic and cultural opportunities. Yet these selection effects (i.e., poor people moving into a geographic area in the hopes of upward mobility, which is generally considered a good thing) is drastically different from the observed outcomes (i.e., large disparities in people's wealth due to their social positions in a system of occupations, which is generally considered a bad thing). Yet Glaeser conflates the two, confusing the reader and, perhaps, himself. A more accurate title for the article would have been "Why poor people moving into Boston isn't a bad thing." This raises a question: why are economists so (consistently) led astray about the causes and consequences of economic, social, and political inequality?

Popularity of Programming Languages

As you can see, R is relatively popular (but more so on StackOverflow than GitHub):

For the original graph, click here. This scatter plot is a reminder that R is useful to learn not only for statistical modeling (since there are so many excellent packages available), but also as a way to become familiar with programming more generally.

Big Science and Sociology

I highly recommend this video featuring Dirk Helbing, a sociologist and erstwhile physicist who is (along with others) attempting to create a CERN-like society-simulating project for the social sciences by combining information from large data sets with simulated models of complex social systems:

Statistical Lexicon

Anyone doing statistical analysis (or contemplating it) should read Andy Gelman's informative, humorous, and dead-on correct post on statistical lexicon.

McKinsey on Big Data

McKinsey has a full report (from March 2011) describing the meaning and potential impact of so-called big data. You can read the report here. One problem, which the authors of the report do not discuss in detail, is the that since so much of what constitutes big data will be collected by private firms there are possibilities of restricted information pockets. In other words, only certain private actors will have access to big data, and academics might very well be left very few big data sources.

Inequality: Everyone's Thinking About It

I ran into the following articles on inequality, which has not only been increasing structurally but culturally (in that more policy elites and journalists are discussing the topic openly). Here are some recent posts on inequality:

• Reuters is reporting findings from a group of researchers showing that Sweden has undergone an enormous increase in inequality, especially since the rise of the center-right in the political system. For those of us in the United States who look to Sweden as a model of development, in recent years even this country has regressed from the ideals of social democracy.
• Based on an online survey (with all the caveats about sampling procedures, of course), a group has surveyed wealthy Americans on their views on inequality. The biggest finding, which reinforces the importance of class-based analyses of electoral politics: among the wealthy there is a huge gap between self-identified Republicans and Democrats, with over 84% of the latter favoring policies taxing the rich while around 29% of the former.

Universal Limits in High-Dimensional Statistics

The MIT Center on Operations Research is hosting a talk tomorrow on universal limits in high-dimensional statistics. The basic idea is that, for all fields of empirical study from sociology to high-energy physics, some criterion for "statistical significance" is crucial for making decisions based on the data. (The current hunt for the Higgs Boson particle is in fact based on a modified criterion for statistical significance.) The problem, however, is that we are entering a world of big data, in which data structures have many dimensions, thus altering the potential usefulness of such criterion for statistical significance.