This paper introduces Decision Physics, a novel theoretical framework that models decisions as irreversible state transitions under uncertainty. While modern artificial intelligence systems excel in prediction and optimization, they lack a formal structure for decision-making in environments characterized by risk, time dependency, and irreversibility. We propose a multidimensional risk formulation incorporating probability, impact, irreversibility, time, and uncertainty, and derive a dynamic system governing risk evolution. The framework is supported by axiomatic foundations, differential equations, and theoretical proofs establishing stability and collapse conditions. Through Monte Carlo simulations and domain-specific applications in finance and energy systems, we demonstrate that incorporating irreversibility significantly improves decision stability and reduces systemic risk. The proposed framework shifts artificial intelligence from predictive modeling toward decision-governance infrastructure, enabling reproducibility, auditability, and scientifically grounded decision-making. This work contributes to decision science, artificial intelligence, and risk theory by introducing a unified mathematical approach to decision systems operating under uncertainty.
Yasin Kalafatoglu (Sun,) studied this question.