Dynamic environmental management and liability attribution using an AlphaZero-Bayes framework: Intelligent decision...

Attributing liability in environmental systems involving multiple strategic actors poses significant challenges for policy-makers and regulators, particularly under conditions of uncertainty, feedback dynamics, and distributed responsibility. Traditional deterministic models of causation are often inadequate for such complex contexts. In this study, we propose a novel hybrid framework that integrates AlphaZero-based reinforcement learning with Bayesian probabilistic inference to construct an intelligent decision support system for multi-agent environmental liability attribution. Our primary motivation is to solve the very difficult legal causality puzzles in environmental fields by making a Gestalt leap, offering more legitimate, intelligent and consistent solutions than those currently found in the literature. While this work does not exhaust the full landscape of such puzzles, its principal contribution is to stimulate further inquiry and open new horizons for computational legal reasoning. The framework introduces a Dynamic Causation Index (DCI) that quantifies each agent's simulated contribution to ecological harm and updates their posterior responsibility using Bayesian inference. AlphaZero models the actors' long-term strategic behavior within environmental and regulatory environments, while the Bayesian layer incorporates historical priors and likelihoods derived from simulation outcomes. This enables both counterfactual analysis and probabilistic responsibility estimation, overcoming key limitations in current environmental decision-making practices. We apply this framework to a hypothetical river pollution case study involving three industrial facilities, demonstrating how the model supports transparent, proportionate, and adaptive allocation of liability. The results show that Factory B bears the highest causal share (55.1%), followed by Factory A (37.5%) and Factory C (7.4%), based on their strategic leverage and posterior responsibility estimates. The results illustrate how strategic leverage and probabilistic confidence can be combined to enhance environmental governance and intervention planning. The proposed methodology offers a scalable and explainable approach to regulatory design and system-level environmental accountability, with potential applications across sustainability science, environmental law, and intelligent governance.