A Large Language Model-Driven Strategic Evaluation Framework via Time-Series Directed Acyclic Graphs

Strategic evaluation is essential for decision-making under uncertainty. Yet existing qualitative and quantitative methods—including chat-oriented large language model (LLM) evaluations—are difficult to deploy in complex, dynamic environments. They often fail to represent nonlinear causal dependencies among indicators, account for temporal lags, or support scalable reasoning. To address these limitations, we propose an LLM-driven strategic evaluation framework with three innovations. First, the framework integrates LLMs across the evaluation lifecycle and couples their qualitative reasoning with quantitative model computation, improving both efficiency and deployability. Second, we introduce a Time-Series Directed Acyclic Graph (TS-DAG) indicator system that explicitly encodes causal structure and time-lagged interdependencies. Third, we develop an LLM-driven procedure that automatically derives the TS-DAG architecture and instantiates its computational parameters, reducing reliance on expert-only construction. We validate the framework through an empirical study of the new energy vehicle market, complemented by baseline algorithm comparisons and sensitivity analyses. The results show that the proposed framework can uncover core indicators, capture competitive dynamics, and explain long-term strategic outcomes across varying environmental conditions. Overall, the framework provides a robust solution for strategic evaluation in complex settings, bridging qualitative strategic reasoning and quantitative, data-driven analysis.