Are We Missing the Target? A Bias-Variance Perspective on Multi-Hazard Risk Assessment

Abstract

This paper presents a generalization of the bias-variance tradeoff applied to the recent trend toward natural multi-hazard risk assessment. The bias-variance dilemma, a well-known machine learning theory, is presented in the context of natural hazard modeling. It is then argued that the bias-variance statistical concept can provide an analytical framework for the necessity to direct efforts toward systemic risk assessment using multi-hazard catastrophe modeling and inform future mitigation practices.