Probabilistic Co-Control for Urban and Environmental Risk

Abstract

This review examines probabilistic control for urban, environmental, and financial risk signals through an evidence-centered design lens. The analysis asks how uncertainty should govern automated escalation. It treats the relevant unit as a complete pathway from data or physical observations to representation, model output, human interpretation, and accountable action. The cited literature is synthesized without inventing experiments or unreported performance values. Particular attention is given to risk scores from different domains being treated as comparable. The review argues that credible translation requires explicit evidence boundaries, uncertainty-aware evaluation, author-visible traceability, and a documented route for intervention. The resulting framework supports urban safety and regulated analytics while distinguishing component promise from system readiness.