A Methodological Framework for Venture Capital Investment Decision Making Using Hybrid MCDM and Machine Learning

This chapter proposes a practical framework for venture capital (VC) investment decisions that combines multi-criteria decision making (MCDM) with machine learning (ML) in a human-in-the-loop setting. It focuses on early-stage VC, where decisions must be made under high uncertainty, limited data, and changing definitions of startup success. MCDM is used to capture expert value judgments as explicit criteria and weights, while ML models estimate criterion scores from observable signals such as innovation, team, traction, governance, and networks. The chapter explains the two-way integration between MCDM and ML, shows how explainable AI artifacts support auditability and learning, and illustrates, through case studies, how hybrid rankings can be turned into personalized, constraint-aware recommendation engines for different investor profiles.