There seems to be consensus that conventional economic models did a poor job predicting the magnitude of the last crisis and that we need to do better. In today's Wall Street Journal, we see the beginnings of one response:
In short, a physicist, a psychoanalyst and an economist believe that they can build a bigger model that captures the complexities of the real world and does a better job of forecasting the future. Good luck with that! While I wish them well, my response is that this will go nowhere or worse, go somewhere bad.
To those who believe that complex models with more variables are the answer to uncertainty, my response is a paper by Ed Lorenz in 1972, entitled Predictability: Does the flap of a butterfly's wing in Brazil set off a tornado in Texas?, credited with creating an entire discipline: chaos theory. In the paper, Lorenz noted that very small changes in the initial conditions of a complex models created very large effects on the final forecasted values. Lorenz, a meteorologist, came to this recognition by accident. One day in 1961, Lorenz inputted a number into a weather prediction model; he entered 0.506 as the input instead of 0.506127, expecting little or no change in the output from the model. What he found instead was a dramatic shift in the output, giving rise to a Eureka moment and the butterfly effect. (One of my favorite books on the topic of Chaos is by James Gleick. It is an easy read and well worth the time.. for investors and economists)
Complex models work best with inputs that behave in thoroughly predictable ways: software and engineering models come to mind. They break down when the inputs are noisy and the relationships are unstable: macro economic models are perfect lab experiments for chaos. The subjects (human beings) belong in strange and unpredictable ways, the variables that matter keep shifting and the relationships between them change over time. In fact, I will wager that the models that worked worst during the last crisis were the most complex models with dozens of inputs and cross relationships.
So, what is the solution? My experience in valuation suggests that you should go in the other direction. When faced with more uncertainty, strip the model down to only the basic inputs, minimize the complexity and build the simplest model you can. Take out all but the key variables and reduce detail. I use this principle when valuing companies. The more uncertainty I face, the less detail I have in my valuation, recognizing that my capacity to forecast diminishes with uncertainty and that errors I make on these inputs will magnify as they percolate through the valuation. More good news: if I am going to screw up, at least I will do so with a lot less work!!