economics

The Guardian has printed an excerpt of economics Nobelist Daniel Kahneman's new book, Thinking, Fast and Slow. The story addresses the question of why investment bankers believe they are doing anything useful when statistics show they average worse than the market. Kahneman draws upon his own experience as a psychologist in the Israeli Army, predicting leadership:

We were willing to make that admission because, despite our definite impressions about individual candidates, we knew with certainty that our forecasts were largely useless. The evidence was overwhelming. Every few months we had a feedback session in which we learned how the cadets were doing at the officer training school and could compare our assessments against the opinions of commanders who had been monitoring them for some time. The story was always the same: our ability to predict performance at the school was negligible. Our forecasts were not much better than blind guesses.

I think the army story is much more interesting than what he describes about his later interactions with investment houses. Using methods developed by the British army, Kahneman's outfit attempted to evaluate leadership potential in candidate officers based on their interactions on a physical task that required teamwork. They believed their assessments to be reliable and based on real data, and yet they did very poorly compared to the officers who ultimately trained the men.

Predicting leadership seems much more relevant to our evolutionary history than predicting investment returns. In many ways, leadership is a much less predictable game than investment. Kahneman describes the interactions among men who may not have previously worked together. But in the absence of reputation and repeated interactions, some effective strategies for attaining and defending status as a leader simply won't work. More aggressive leadership strategies may work in the short term, while judgment and fairness become important among people who know each other well.

The usual argument for why we don't predict marginal probabilities well is that we haven't encountered them in nature. But here's a task it seems we ought to be pretty good at. And maybe we aren't. Worth further exploration.

The NY Times has a profile of economist Robert W. Fogel ("Technology Advances; Humankind Supersizes"). Fogel, along with other historical economists, has worked to document the changes in human stature, mass and health during the last few hundred years. These changes were mostly not evolutionary. That is, it wasn't genetic change that made us bigger, for the most part.

The documentation of these trends has made for a fascinating series of historical studies. The occasion for the profile is the upcoming release of a book by Fogel and colleagues summarizing the decades of work.

To take just a few examples, the average adult man in 1850 in America stood about 5 feet 7 inches and weighed about 146 pounds; someone born then was expected to live until about 45. In the 1980s the typical man in his early 30s was about 5 feet 10 inches tall, weighed about 174 pounds and was likely to pass his 75th birthday.

Across the Atlantic, at the time of the French Revolution, a 30-something Frenchman weighed about 110 pounds, compared with 170 pounds now. And in Norway an average 22-year-old man was about 5 ½ inches taller at the end of the 20th century (5 feet 10.7 inches) than in the middle of the 18th century (5 feet 5.2 inches).

This stuff is tremendously important for human biologists to understand, and the data have become enormously richer in many respects as historical economists have drawn together records about military conscripts, food allowances and disease rates.

The second part of the profile goes into some areas of criticism for Fogel (he focuses mainly on nutrition, other scholars argue for the importance of different causes). I think it is time to integrate a more evolutionary view into the data on recent secular trends. The Framingham study and other longitudinal surveys have demonstrated differential fertility associated with stature in contemporary industrialized societies. Evolution is happening, and does not necessarily go in the same direction as the secular increase in stature. Meanwhile, population differences in stature and other traits owe to a deeper history that includes different causes.

You're not coming here for economic analysis, but I found this Wired article on quants, risk, and the financial crisis useful:

Bankers should have noted that very small changes in their underlying assumptions could result in very large changes in the correlation number. They also should have noticed that the results they were seeing were much less volatile than they should have been—which implied that the risk was being moved elsewhere. Where had the risk gone?

They didn't know, or didn't ask. One reason was that the outputs came from "black box" computer models and were hard to subject to a commonsense smell test. Another was that the quants, who should have been more aware of the copula's weaknesses, weren't the ones making the big asset-allocation decisions. Their managers, who made the actual calls, lacked the math skills to understand what the models were doing or how they worked. They could, however, understand something as simple as a single correlation number. That was the problem.

These models are not so different from genetic analyses, and in fact phenotype prediction on the basis of genome-wide SNP data or sequences will likely involve many of the same problems. In particular, the problem of testing for correlations with limited data is one that I run up against with phenotype evolution quite often.

On the same topic, Nassem Taleb's essay, "The Fourth Quadrant" is also useful in understanding the present difficulties.