Coping with Nasty Surprises: Improving Risk Management in the Public Sector Using Simplified Bayesian Methods

Abstract

Bayesian methods are particularly useful to informing decisions when information is sparse and ambiguous, but decisions involving risks must still be made in a timely manner. Given the utility of these approaches to public policy, this article considers the case for refreshing the general practice of risk management in governance by using a simplified Bayesian approach based on using raw data expressed as ‘natural frequencies’. This simplified Bayesian approach, which benefits from the technical advances made in signal processing and machine learning, is suitable for use by non-specialists, and focuses attention on the incidence and potential implications of false positives and false negatives in the diagnostic tests used to manage risk. The article concludes by showing how graphical plots of the incidence of true positives relative to false positives in test results can be used to assess diagnostic capabilities in an organisation — and also inform strategies for capability improvement.

Keywords: risk, management, Bayesian inference, public sector