The Multivariable Dynamic Causality Model (MCDM): A Framework for Real-Time Prediction in Complex Systems

Predicting events in highly complex and interconnected environments has long been one of the greatest challenges in science and engineering. While traditional probabilistic models often fail to anticipate high-impact disruptions — and theories such as Taleb's "Black Swan" advocate for radical unpredictability — this paper introduces the Multivariable Dynamic Causality Model (MCDM) as an alternative framework. MCDM posits that most events are not inherently random but arise from dynamic, real-time causal interactions among thousands of interdependent variables, many of which are latent or unobserved. By integrating real-time data, adaptive learning, and multivariable causality, MCDM aims to provide a theoretical foundation for increasingly precise forecasting of complex system behaviors. This paper outlines the core principles of MCDM, discusses its philosophical implications, and explores potential applications in engineering, risk modeling, and decision-making under uncertainty.