Author: Sanjay Srivastava

Behavioral scientist

The real world of Wall Street

Published on by Sanjay Srivastava

People often contrast “the real world” against the world of academia. (The conjunction of “real world” and “academia” gets over a million hits on Google. I assume that only a tiny fraction refer to humanities dissertations about the MTV show.)  The idea is that we live in a world of ideas, protected from reality. We traffic in fuzzy abstractions, and the things we do are only tenuously connected to anything of tangible value to the world that most people live and work in.

I happen to think that’s not a very accurate view of academic psychology. But it has occurred to me that the indictment against the ivory tower seems to apply quite well to the supposedly real world of Wall Street finance over the last couple of decades.

That observation jumped out at me reading Wired‘s March cover story on quantitative finance. The article focuses on a statistician, David Li, who chose Wall Street over academia. Li came up with new a way to calculate the probability that a bundle of securities would all default at once. Previously, this had been done by looking at the historical performance of similar securities, which meant that new types of securities could not be priced with much confidence. Li’s approach bypassed the historical approach and quickly became the de facto standard on Wall Street:

As a result, just about anything could be bundled and turned into a triple-A bond—corporate bonds, bank loans, mortgage-backed securities, whatever you liked. The consequent pools were often known as collateralized debt obligations, or CDOs. You could tranche that pool and create a triple-A security even if none of the components were themselves triple-A. You could even take lower-rated tranches of other CDOs, put them in a pool, and tranche them—an instrument known as a CDO-squared, which at that point was so far removed from any actual underlying bond or loan or mortgage that no one really had a clue what it included. [emphasis added]

As other observers have pointed out before, a major problem with the derivatives market was that it was so far removed from the actual value of whatever the derivatives were derived from — mortgaged houses, the capital and products of a corporation, etc. Wall Street, in other words, was trading abstractions without really thinking about their relationship to the real world. Sound familiar?

And what’s especially interesting about the Wired article is who it credits for sounding the alarm bells (which were ignored by Wall Street for a decade). It was a bunch of academics:

Investment banks would regularly phone Stanford’s Duffie and ask him to come in and talk to them about exactly what Li’s copula was. Every time, he would warn them that it was not suitable for use in risk management or valuation.

Real world, indeed.

Voting nay on Pi Day

Published on by Sanjay Srivastava1 Comment

A math joke has gone legit. Congress has designated 3/14 Pi Day.

The vote passed overwhelmingly — but over at New Scientist, Ewan Callaway inquires about the 10 representatives who voted against the resolution.

One of the names jumped out at me — Randy Neugebauer (R-TX). A few years ago, Rep. Neugebauer tried to attach an amendment to an appropriations bill circumventing scientific peer review and attacking the NIH-funded research of two psychologists, Sam Gosling and Laura King. Gosling studies how physical environments are shaped by the psychological characteristics of people who live in them. King studies how expressive writing promotes mental health. During debate over the amendment, Neugebauer misrepresented their work in order to ridicule it, for example dismissing Gosling as studying “dorm wall decorations.”

I haven’t been able to find a statement from Neugebauer about why he voted against Pi Day. (His fellow anti-Pi voters haven’t given very compelling reasons.) Given his past record, I have to wonder.

Making progress in the hardest science

Published on by Sanjay Srivastava8 Comments

The name of this blog comes from a talk I gave at the SPSP conference.

The talk was in a training symposium for people starting out in academic psychology. People at various stages of their careers were invited to talk about how we approach research. I titled my talk “Making Progress in the Hardest Science,” and the first third of the talk was a half-serious, half joking explanation of the title.

The idea is that you often hear people arrange the sciences on a continuum from “hard” to “soft,” with physics at the hard end and psychology at the soft end. The implicit message is that the “hard” sciences are more scientific. But that’s not based on anything fundamental or substantive. As best as I can tell, it’s about scienciness. We have these preconceptions and stereotypes about what science is supposed to be about — big fancy equipment, lab coats, etc. But that’s not science. That’s the superficial sheen of scienciness.

What science is is a method of inquiry. In a nutshell, it’s the application of logic to empirical evidence. And by that measure, physics and biology and psychology are equally and fully scientific, because we’re all trying to figure stuff out by systematically gathering evidence and applying logic to it. (Or at least our respective academic versions are. I offer no defense of the Doctor Firstnames in your local bookstore.)

So it doesn’t make sense to try to determine who is more scientific. Instead, what differs is what we are trying to figure out — the phenomena we strive to understand. And here I think the other meaning of “hard” is useful.

What are the “hard” — as in difficult — problems in science? Hard problems in science are those that are embedded in complex systems; they are hard because to study something well you often need to isolate it from outside influences. Hard problems are those that vary by local conditions — science seeks to identify general laws, and when something is locally dependent, you need to sniff out the complex interactions that make it so. And hard problems are those that are difficult to quantify — science rests upon formalization and quantification, and you need to get traction at that initial step of quantification (i.e., measurement) before you can test theories. So… by these measures, if we are going to differentiate areas of science, the continuum of scientific problems should go from “hard” to “easy,” and psychology is clearly a science that deals with hard problems. Perhaps the hardest.

This was mostly intended as cheerleading to an audience of budding academic psychologists, revolving around a debatably clever pun. (I don’t really mean to suggest that physicists and chemists are picking the low-hanging fruit of the scientific disciplines. The low branches were picked clean centuries ago.) But the human mind is an amazingly complicated thing to study, which is what makes psychology so much fun.