Visual modeling of the probabilistic path of Taiwan's unification strategy --A Causal Inference Model Based on Bayesian Networks and Markov Simulation

This study proposes a set of AI-driven frameworks for conducting visual modeling and probabilistic simulation of strategic pathways in cross-strait unification scenarios focusing on the Taiwan Strait issue. The model incorporates Bayesian networks to capture causal dependencies among key variables such as economic integration, political reconfiguration, military deterrence, and international intervention; employs Markov chains to simulate the evolution of long-term social opinion in Taiwan; and evaluates optimal unification strategies under multiple scenarios via a decision tree algorithm. The framework also introduces system dynamics techniques and advanced visualization tools to enhance the interpretability and decision support functions of the model.The quantitative simulation results show that economic dependence is the core driving force for peaceful unification. When Taiwan's economic dependence on China exceeds 60%, the probability of peaceful unification rises to over 70% within 20 years. Sustained military deterrence significantly reduces the likelihood of direct external intervention, and the probability of successful military unification is significantly higher in a scenario where the U.S. and Japan's military response is limited. The study also finds that a shift in the island's political ecology is crucial: the dominance of pro-continental parties increases the probability of reunification by more than 30%.In terms of short-term unification strategies (within 5 years), simulation results show that the probability of successful unification can be over 85% if high-intensity military pressure, economic blockade and political polarization are adopted, and the U.S. and Japan's military intervention is effectively blocked. However, such strategies come with significant geopolitical risks. To reduce the risk, the model suggests supplementing the tactics with strategic deception, diplomatic mediation, psychological warfare, and precision military operations.This study proposes a transferable modeling approach for simulating complex geopolitical scenarios, combining probabilistic insights with dynamic visualization analysis, to provide scientific support for strategic planning and policymaking.