TRI-INDEX: A Geometric–Causal Architecture for Strategic World Modeling and Policy Simulation

Modern socioeconomic and geopolitical systems exhibit complex nonlinear interactions across technological, institutional, and economic domains. Traditional analytical tools—including econometrics, machine learning, and agent-based simulation—capture partial aspects of these systems but lack an integrated framework capable of representing relational knowledge, structural causality, and dynamic system behavior simultaneously. This paper introduces TRI-INDEX (Total Relational Intelligence Index), a geometric–causal computational architecture designed for strategic world modeling and policy simulation. The framework integrates semantic representation, knowledge graph construction, structural causal modeling, and hybrid dynamical system simulation within a unified computational pipeline. The architecture transforms heterogeneous multimodal information—including text, structured data, and relational networks—into a causal world model capable of supporting counterfactual policy evaluation, scenario analysis, and systemic risk monitoring. The framework produces a composite metric, the TRI score, defined as a weighted aggregation of four dimensions: structural knowledge quality, causal coherence, dynamic system performance, and systemic resilience. By combining advances in knowledge representation, causal inference, and complex systems modeling, TRI-INDEX provides a foundation for computational policy intelligence and strategic decision support in complex adaptive environments.