Analyzing the Effect of Model Robustness on Statistical Decision-Making in Risk Assessment and Forecasting

Posted: Apr 11, 2023

• Claire Cook
• Connor Edwards
• Daniel Wood

Abstract

The proliferation of machine learning models in high-stakes decision-making domains has intensified the need for robust and reliable predictive systems. Traditional evaluation metrics such as accuracy, precision, and recall provide limited insight into how models perform under real-world conditions characterized by distribution shifts, adversarial perturbations, and temporal dynamics. While substantial research has focused on improving model robustness against specific threats, the fundamental question of how robustness properties translate to improved decision outcomes remains largely unanswered. This paper addresses this critical gap by systematically analyzing the relationship between model robustness characteristics and decision quality across multiple risk assessment and forecasting domains. Risk assessment and forecasting applications present unique challenges for statistical decision-making. These domains typically involve sequential decisions under uncertainty, where the consequences of poor predictions can be severe. Financial institutions rely on forecasting models for investment decisions and risk management, public health organizations use predictive models for disease outbreak response, and environmental agencies employ risk assessment models for disaster preparedness. In each case, the robustness of underlying statistical models directly impacts the quality of decisions made by human operators or automated systems. Our research introduces a novel framework for evaluating robustness-decision alignment that moves beyond conventional robustness metrics. We define robustness not merely as resistance to adversarial attacks, but as a multi-dimensional property encompassing distributional shift resilience, temporal stability, uncertainty quantification reliability, and operational consistency. This comprehensive approach allows us to examine how different aspects of robustness contribute to decision quality in practical scenarios. The primary contributions of this work are threefold. First, we develop a methodology for quantifying the relationship between model robustness and decision outcomes across diverse application domains. Second, we conduct extensive empirical analysis to identify which robustness characteristics most significantly impact decision quality.