Taking uncertainty seriously: simplicity versus complexity in financial regulation

Distinguishing between risk and uncertainty, this article draws on the psychological literature on heuristics to consider whether and when simpler approaches may outperform more complex methods for modeling and regulating the financial system. We find that: simple methods can sometimes dominate more complex modeling approaches for calculating banks' capital requirements, especially when data are limited or underlying risks are fat-tailed; simple indicators often outperformed more complex metrics in predicting individual bank failure during the global financial crisis; when combining different indicators to predict bank failure, simple and easy-to-communicate fast-and-frugal decision trees can perform comparably to standard, but more information-intensive, regressions. Taken together, our analyses suggest that because financial systems are better characterized by uncertainty than by risk, simpler approaches to modeling and regulating financial systems can usefully complement more complex ones and ultimately contribute to a safer financial system.

Automated summary

The study suggests that simple methods may outperform more complex approaches in calculating bank capital requirements and predicting individual bank failures, especially when data are limited or risks are fat-tailed. Additionally, combining different indicators to predict bank failures, simple and easy-to-communicate decision trees can perform comparably to standard, information-intensive regressions.