Apologies, once again, for the blogging silence. I was busy in London, on tour for the UK version of the book, which is called “The Decisive Moment”. (We got some great press, including being featured as “Book of the Week” by BBC Radio 4.)
Although book tours can, on occasion, be frustrating and grueling – I’m so sick of airport food that I don’t even like Egg McMuffins anymore, and I’m getting to the point where I detest the sound of my own voice – one of the genuine highlights is getting to answer questions from your readers. As an author, there is nothing more exciting than learning which parts of the book people find interesting and want to know more about or which points they disagree with or which stories resonate with their own experience. It’s the thrill of seeing your words enter the world, of seeing a mass of pixels in a word processor become a collection of ink blots on pages made from dead trees. And then, because these ink blots are arranged just so, they can enter the mind of someone else, so that your sentences get remixed and reanalyzed. A single idea, typed more than a year ago on a laptop keyboard, has multiplied itself into a swarm of ideas.
One of the most interesting questions I’ve gotten while on tour goes something like this: “Given the massive decision-making flaws exposed by the current economic mess, what
variables should scientists be investigating in the future so that we can better understand how we got here? In other words, what will be the hot topic in decision-making science two years from now?”
Here’s my sputtering answer: I think the financial crisis has helped expose a powerful bias in human decision-making, which is our abhorrence of uncertainty. We hate not knowing, and this often leads us to neglect relevant information that might undermine the certainty of our conclusions. I think some of the most compelling research on this topic has been done by Colin Camerer, who has played a simple game called the Ellsberg paradox with subjects in an fMRI machine. To make a long story short, Camerer showed that players in the uncertainty condition – they were given less information about the premise of the game – exhibited increased activity in the amygdala, a center of fear, anxiety and other aversive emotions. In other words, we filled in the gaps of our knowledge with fright. This leads us to find ways to minimize our uncertainty – we can’t stand such negative emotions – and so we start cherry-picking facts and forgetting to question our assumptions.
A similar phenomenon is also at work when we’re confronted with too many equivalent options, which is what happens to me every time I have to pick a toothpaste. There’s some suggestive evidence by Akshay Rao that this “trade-off aversion” – do I want the Colgate Total or the Crest Pro-Health? – leads to increased activation in areas that are often associated with cognitive conflict and the detection of errors, such as the anterior cingulate cortex. This helps explain why I start getting anxious whenever I near the toothpaste aisle of the supermarket.
While Camerer’s experiment is fascinating, I’d love to see data from a more realistic set of experiments. Why not bring in actual investment bankers and watch how they respond to varying levels of information, or how giving them a quantitative model that’s supposed to assess risk (and thus remove the uncertainty) alters their decision-making process?
Which brings me to Dennis Overbye’s fascinating analysis of “The Wall Street Physicists”. He looks at how the rise of quants bearing impossibly complicated mathematical formulas gave financial firms a new kind of confidence to engage in risky trades and investment innovations:
The Black-Scholes equation resembles the kinds of differential equations physicists use to represent heat diffusion and other random processes in nature. Except, instead of molecules or atoms bouncing around randomly, it is the price of the underlying stock.
The price of a stock option, Dr. Derman explained, can be interpreted as a prediction by the market about how much bounce, or volatility, stock prices will have in the future.
But it gets more complicated than that. For example, markets are not perfectly efficient — prices do not always adjust to right level and people are not perfectly rational. Indeed, Dr. Derman said, the idea of a “right level” is “a bit of a fiction.” As a result, prices do not fluctuate according to Brownian motion. Rather, he said: “Markets tend to drift upward or cascade down. You get slow rises and dramatic falls.”
One consequence of this is…that when you need financial models the most — on days like Black Monday in 1987 when the Dow dropped 20 percent — they might break down. The risks of relying on simple models are heightened by investors’ desire to increase their leverage by playing with borrowed money. In that case one bad bet can doom a hedge fund. Dr. Merton and Dr. Scholes won the Nobel in economic science in 1997 for the stock options model. Only a year later Long Term Capital Management, a highly leveraged hedge fund whose directors included the two Nobelists, collapsed and had to be bailed out to the tune of $3.65 billion by a group of banks.
The collapse of LTCM is a microcosm of so many of the cognitive flaws that led us to the current mess. Because everybody at LTCM believed in the state-of-the-art model, few were thinking about how the model might be catastrophically incorrect. The hedge-fund executives didn’t spend enough time worrying about the fat tail events that might disprove their theories. Instead, they pretended that the puzzle of the marketplace had been solved – the uncertainty of risk had been elegantly quantified.
Once this happens, we start making serious mistakes. The errors inherent in the model are compounded by our desire to prove the model right. Instead of using our reasoning powers to improve our predictions, we use reason to reassure ourselves, to rationalize away the warning signs of failure. Our sense of certainty – the model must be right – is dishonestly preserved. And so LTCM ignored the brewing troubles in the Asian markets. The executives discounted the rumors that Russia might default. They ruled out the possibility of a market meltdown, which led them to take massive risks that didn’t appear risky. Because LTCM was making decisions under the spell of certainty, they ended up making a series of dangerous decisions.
Replace LTCM with, well, just about every major financial firm, and replace Russian and Asian markets with “subprime debt,” and it’s the same old story. Models can be a crucial decision-making tool, but they can also lead us to disaster. This isn’t the fault of the models, or even the quants – it’s the fault of all those executives who used these models they didn’t really understand to silence their amygdala, so that their fear of risk disappeared. They were certain there was little to worry about, which is generally a sign that we should start getting scared.