The principles behind IB-Net's design can be adapted for different types of decision-making processes outside Boolean satisfiability by customizing the input data representation and output predictions according to the specific domain requirements. For example, in financial trading algorithms, similar graph neural network architectures could be used to analyze market trends and predict optimal investment strategies based on historical data patterns. In supply chain management, IB-Net's approach could assist in optimizing inventory levels and distribution routes by modeling complex dependencies between suppliers, warehouses, and demand forecasts using graph representations.