Buying a car is a hard decision. There are just so many variables to think about. We’ve got to inspect the interior and analyze the engine, and research the reliability of the brand. And then, once we’ve amassed all these facts, we’ve got to compare different models.
How do we sift through this excess of information? When consumers are debating car alternatives, studies show that they tend to focus on variables they can quantify, such as horsepower and fuel economy…
Unfortunately, this obsession with horsepower and fuel economy turns out to be a big mistake. The explanation is simple: The variables don’t matter nearly as much as we think…Although we like to imagine ourselves as Steve McQueen, accelerating into the curves, we actually spend most of our driving time stuck in traffic, idling at an intersection on the way to the supermarket.
-The Sabermetric Bias, Jonah Lehrer
When I first read those paragraphs, the first thought that came to mind was how my mother would task me with buying a computer for her (I have bought four or five for her). Invariably, she would ask me how fast the CPU was, how much RAM memory it had and how big the hard drive was, when most of the time she surfs the net or does word processing. Usually I just picked a price point and tried to find the best computer I could get for her at that price.
But when I thought about what Lehrer had to say a second time, it occurred to me that this is exactly the same situation the economics field finds itself in. The economy is complex in the extreme. And what economists tend to do is model it by making a few simplifying and clarifying assumptions. Here’s the problem as Rick Bookstaber put it:
A complex system is one that is difficult to understand and model; as complexity increases, so do the odds of something unanticipated going wrong. This is the driving characteristic of complexity that is most important for finance and economics: complexity generates surprises, unanticipated risk. “Unanticipated” is the key word: it is not simply that more complexity means more risk — we can create risk by walking on a high wire or playing roulette. Rather, it is that complexity increases risk of the “unknown unknowns” variety…
The limits of knowledge arising from this definition of complexity are not randomness or prediction error. Uncertainty where the states are known but it is uncertain which state will be realized is not a complex system. This distinction has been made by Keynes and Knight between risk and uncertainty, embodied in the concept of Knightian uncertainty.
The problem is that the economics profession is all about models and you can’t model Knightian uncertainty. So what happens then is that a lot of economists end up concentrating on the variables that can be modelled using the typical DSGE model that seems to be standard fare.
The Robert Lucas critique of modelling is based on the known unknowns of shifts in the correlation of macroeconomic variables like unemployment and inflation where the assumption is that there is a trade-off between the two. This is not necessarily the case and even so the trade-off could shift due to structural rigidities or other unknown variables.
But what about the unknown unknowns of Knightian uncertainty? In today’s world of systemically important financial institutions that the Kansas City Fed’s Thomas Hoenig says are “fundamentally inconsistent with capitalism”, there is a certain advantage to increasing complexity that leads to more Knightian uncertainty. It is just this uncertainty that makes these firms too big to fail.
Moreover, given the arguments now being used to bail out Greece, that a default would be “Europe’s Lehman,” due to credit default swap exposures and the asset-liability duration mismatch of the shadow banks, you would think there would be a push by regulators to reduce complexity as Hoenig recommends. There is not.
The lesson, then, for too big to fail institutions and countries alike is that they need to increase complexity, increase risk and reduce transparency because only then will they be assured of a bailout when the inevitable crisis happens. And while the economy is building to that crisis they can extract the rents that go along with this implicit guarantee for their own monetary benefit.
Correct me if I’m wrong, but I don’t see anything that has changed since this crisis. if anything the concentration of risk amongst the systemically important is greater.
Meanwhile economists with their DSGE models, looking for another quick fix cyclical rebound, are like the drunk who is looking for his lost keys under the street lamp because that’s where the most light is, not because that’s where he dropped the keys. May I suggest we look instead to the complexities of our financial system and the enormity of its central players, the too big to fail banks and the shadow banks in the money market and hedge fund world. Let’s get a flashlight and throw some light over there. It may not ‘fix’ the immediate demand shortfall, but it will go a long way toward preventing another crisis.
When making the same case for not relying on numbers alone in Sports, Jonah Lehrer tell us what economists need to do too:
My sole point is that our newfound reliance on data and statistics naturally leads to their misapplication. Because we’re so enamored with the numbers, we tend to undervalue what can’t be compressed into numerical form, even as we pay lip service to the lingering importance of intangibles. This is a cognitive bias we all need to watch out for.