Open AccessCoping with Nasty Surprises: Improving Risk Management in the Public Sector Using Simplified B ayesian Methods
Author(s) -
Matthews Mark,
Kompas Tom
Publication year - 2015
Publication title -
asia and the pacific policy studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.529
H-Index - 14
ISSN - 2050-2680
DOI - 10.1002/app5.100
Subject(s) - false positive paradox , risk management , corporate governance , false positives and false negatives , computer science , risk analysis (engineering) , raw data , actuarial science , artificial intelligence , economics , business , management , programming language
Abstract B ayesian 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 B ayesian approach based on using raw data expressed as ‘natural frequencies’. This simplified B ayesian 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.