A recurrent feature of game-theoretical economics, political science and sociology is the principal-agent problem. Many phenomena in the social world can be described in terms of the (various) theories of principals and agents. Want to understand how Southwest Airlines broke into the industry? Why presidents do not exit losing wars? Why it was an advantage for Kennedy in his standoff with Khrushchev to have a “rogue” general who favored nuclear war? Why corruption is not only a collective action problem? Principal-agent theory is here to help! A principal sends an agent to do a task, under some kind of contract or agreement, establishing a relationship subject to certain constraints, and open to certain possibilities. If we can describe these constraints and possibilities, we can explain a lot. Does agent know more than principal? Does principal have the capacity to punish agent, reward agent, or both? And so on.
It looks like we’re in another “economic downturn,” and many PhDs are understandably worried about what it means for the future of the sociology job market. I haven’t been to Delphi or stayed at a Holiday Inn Express, but I am knee-deep in historical ASA reports. Here’s what the data look like for how the 2008 recession affected the sociology job market in the US. Spoiler: I don’t think the market ever recovered, and more hopeful estimates say it took 4 years to recover.
It’s hard to even begin blogging about something so vast and ever-shifting. This post is just going to be a short pointer to a couple of the best pieces I’ve seen covering the social and economic angles of the pandemic.
The following is a guest post by Juan Pablo Pardo-Guerra.
Topic models are fast emerging as a workhorse of computational social science. Since their introduction in the late 1990s as part of a larger family of classification and indexing algorithms, they have grown into one of the most common and convenient means for automated text analysis. Not too long ago, using topic methods confronted scholars unfamiliar with programming with steep learning curves: even the simplest implementations required some familiarity with coding in addition to a good deal of patience. Today, by contrast, topic modeling is available as part of point-and-click desktop applications (e.g. Context) and can be installed in widely used statistical analysis packages (e.g. Stata). The relative ease, scalability, and intelligibility of topic models explains, perhaps, their quick adoption across sociology, political science, and the digital humanities. Indeed, to say that topic models are the OLS of text analysis wouldn’t be too much of an exaggeration.
Last spring, Nicholas Christakis published his latest book, Blueprint: The Evolutionary Origins of a Good Society. The title already sets the stage for some old-fashioned biological determinism.1 For anyone familiar with evolutionary psychology, it is 520 pages of mostly the same claims, logic, and citations as any other recent evopsych writing aimed at a general public readership. He’s a fan of Steven Pinker’s theses in Enlightenment Now and Blank Slate, and Pinker’s endorsement is prominent on the front cover of Blueprint. For those unfamiliar, the field is riven with “just-so stories,” or simplistic, largely unverifiable assertions about our evolutionary past that justify modern stereotypes and inequalities. The New Yorker summarizes and historicizes this pattern well. When it makes normative claims, evopsych tends to engage in the same is/ought fallacy that structural functionalism did: whatever we observe people doing must serve some necessary function—or else it wouldn’t have evolved—and so whatever is, ought to be. Social reformers beware: you’re meddling with forces beyond your comprehension and fighting against human nature.
Earlier this week, I was asked to help organize an event for graduate students seeking advice on the “responsible use of twitter for grad students.” Of course, my first instinct was to crowdsource advice from #SocTwitter itself. In this post, I gather together some of the advice suggested by others, including a list of already published or posted resources and guides.