Abstract
Abstract
Venture capital decision-making is increasingly influenced by uncertainty, information asymmetry, and the growing volume of unstructured data. Although previous research has advanced analytical methods for portfolio selection, existing approaches rarely integrate dynamic system behaviour with qualitative market intelligence within a unified framework. This study proposes an AI-augmented decision framework that combines digital twins with large language model-based market intelligence to support venture capital investment under uncertainty. The framework integrates structured financial data with real-time qualitative insights from market narratives, while digital twins simulate startup development and scenario-based performance. Embedded within a multi-criteria decision structure, these inputs enable more adaptive, transparent, and interpretable portfolio selection. Empirical findings indicate that the proposed framework improves decision quality by increasing expected returns while reducing risk and prediction error. It also strengthens decision stability and supports more effective evaluation of trade-offs in complex investment environments. The study contributes to the literature on AI-enabled decision-making and innovation finance by demonstrating how emerging technologies can improve investment evaluation and reduce uncertainty in entrepreneurial ecosystems.