Support of Managerial Decision Making by Transductive Learning

Transductive inference has been introduced as a novel paradigm towards building predictive classification models from empirical data. Such models are routinely employed to support decision making in, e.g., marketing, risk management and manufacturing. To that end, the characteristics of the new philosophy are reviewed and their implications for typical decision problems are examined. The paper’s objective is to explore the potential of transductive learning for corporate planning. The analysis reveals two main factors that govern the applicability of transduction in business settings, decision scope and urgency. In a similar fashion, two major drivers for its effectiveness are identified and empirical experiments are undertaken to confirm their influence. The results evidence that transductive classifiers are well superior to their inductive counterparts if their specific application requirements are fulfilled.