Cognitive shadow: A policy capturing tool to support naturalistic decision making

Policy capturing is an approach to decision analysis using statistical models such as multiple linear regression or machine learning algorithms to approximate the mental models of decision makers. The present work seeks to apply a robust policy capturing technique to functionally mirror expert mental models and create individually-tailored cognitive assistants. The “cognitive shadow” method aims to improve decision quality by recognizing probable errors in cases where the decision maker is diverging from his usual judgmental patterns. The tool actually shadows the decision maker by un-intrusively monitoring the situation and comparing its own decisions to those of the human decision maker, and then provides advisory warnings in case of a mismatch. The support methodology is designed to be minimally intrusive to avoid an increase in cognitive load, either in real-time or off-line dynamic decision making situations. Importantly, user trust is likely to be a key asset since the cognitive shadow is derived from one's own judgments. A use case of the cognitive shadow is described within the context of a maritime threat classification task, using the classic CART decision tree induction algorithm for policy capturing. This approach is deemed applicable to a large variety of domains such as supervisory control, intelligence analysis and surveillance in defence and security, and of particular relevance in high-reliability organizations with low tolerance for error.