Decision Systems at the Edge: A Collapse-Aware Framework for Managing Irreversible Transitions in Complex Domains

Modern predictive systems perform well under normal operating conditions but often struggle when complex systems approach irreversible transitions. Across domains such as drug discovery, financial markets, and industrial process control, failure may emerge not as a gradual decline but as an abrupt transition across a critical threshold. This paper presents a conceptual framework for collapse-aware decision systems. Rather than relying solely on prediction, the framework integrates four interacting components: current system state, historical memory, interaction dynamics, and acceleration toward instability. These components are combined into a collapse metric designed to estimate proximity to an irreversible transition boundary and support timely intervention. Illustrative applications in oncology trials, financial risk management, and industrial process control demonstrate how collapse-aware decision systems may provide an earlier and more explicit decision response than conventional predictive or early-warning approaches, particularly where the cost of delayed intervention materially exceeds the cost of acting prematurely. The paper forms part of a broader research and intellectual-property programme concerning collapse-aware computation and decision systems. The system architecture and decision framework described in this publication are associated with: Published U.S. Patent Application Publication No. US 2026/0211387 A1U.S. Patent Application No. 19/571,508 The related wave-interference-based collapse computation mechanism is the subject of: U.S. Patent Application No. 19/651,619 In certain implementations, the computational mechanism may use mathematical models derived from the author’s earlier work on the Total Wave Modified Schrödinger Equation (TWMSE). This paper focuses on the application and decision-system layer rather than the full underlying physical or mathematical derivation. The framework is intended to complement, rather than replace, existing predictive models and early-warning methodologies. It provides an additional decision layer for managing asymmetric risks where adverse outcomes may become difficult or impossible to reverse. The publication is relevant to researchers and practitioners working in artificial intelligence, complex systems, decision science, drug discovery, quantitative finance, systems engineering, process control, and risk management.