Haldane, the Executive Director for Financial Stability at Bank of England, brings up the topic is how to act in situations of uncertainty, and the role of our models of reality in making the right decision. How complex should they be in the face of a complex reality? The answer, based on the literature on heuristics, biases and modelling, and the practical world of financial disasters, is simple: they should be simple.
Using too complex models means that they tend to overfit scarce data, weight data randomly, require significant effort to set up – and tends to promote overconfidence. As Haldane then moves on to his own main topic, banking regulation. Complex regulations – which are in a sense models of how banks ought to act – have the same problem, and also act as incentives for playing the rules to gain advantage. The end result is an enormous waste of everybody’s time and effort that does not give the desired reduction of banking risk.
It is striking how many people have been seduced by the siren call of complex regulation or models, thinking their ability to include every conceivable special case is a sign of strength. Finance and finance regulation are full of smart people who make the same mistake, as is science. If there is one thing I learned in computational biology is that your model better produce more nontrivial results than the number of parameters it has.
But coming up with simple rules or models is not easy: knowing what to include and what not to include requires expertise and effort. In many ways this may be why people like complex models, since there are no tricky judgement calls.