Investment firms increasingly rely on complex quantitative models whose empirical support is often mixed, incomplete, and evolving. Traditional model validation frameworks, which rely on binary pass–fail judgments applied at discrete points in time, are poorly suited to this reality and provide limited guidance for ongoing oversight, escalation, or retirement decisions. This article proposes an evidence-based governance framework that treats model evaluation as a dynamic, continuous process rather than a static certification exercise. Building on Belnap’s four-valued logic, we extend many-valued reasoning to a two-dimensional continuous truth space defined by empirical success and empirical consistency. We then integrate temporal dynamics, governance states, and threshold-based decision triggers that map evidential profiles directly to oversight regimes and mandated actions. The framework enables firms to differentiate governance responses across models with similar performance but fundamentally different risk characteristics, enforce institutional discipline through pre-specified escalation rules, and maintain accountability as evidence evolves. By formalizing the relationship between empirical evidence and governance response, the proposed approach offers investment organizations a practical methodology for managing model risk across the full lifecycle—from deployment to monitoring to retirement—while supporting stakeholder protection, regulatory compliance, and organizational transparency.
Joseph Simonian (2026) studied this question.