Designing Tools to Support Cognitive Decision Making Under Uncertainty

点击翻译按钮获取中文摘要

High-risk industries (e.g., aviation, naval, nuclear power, healthcare) are vulnerable to safety failures because of the complexity imposed by the interaction between human operators and their decision-support tools. Uncertainty introduces additional complexity because operators must collaborate with the decision-support tools to detect and appropriately manage the uncertainty when it arises. Understanding how to support operators' Cognitive Decision Making (CDM) during uncertainty is critical to designing decision-support tools to engender the right behaviours at the right time. Current decision-support tools don't fully support operator decision-making. Operators continue to experience cognitive overload, misinterpret displays, and mistrust signals, resulting in inaccurate decisions. Automation can be effective for well-defined circumstances where knowledge and evidence around optimal practices are understood. In reality, however, operators are frequently confronted with evolving situations to which they must adapt, and in which automated approaches are no longer appropriate because the anticipated context for which they were designed has changed. Operators must switch between automation and adaptive CDM depending on the level of uncertainty, which can stem from the situation (e.g., loss of time awareness), environmental factors (e.g., loss of power), and operators' skill/knowledge. Uncertainty is precipitated when usual rhythm is disrupted, and can range from mundane to catastrophic events. We must design tools to support operators to effectively engage automation and adaptive CDM when managing safety risks. How do we control for unnecessary variation and recognize, value, and support necessary variation? This is a complex, multi-dimensional question and is an incredibly important area that has by and large been ignored. The long-term objective of this research program is to optimize CDM quality with the introduction of decision-support tools that leverage predictive analytics to detect and communicate uncertainty to operators. The short-term objectives are to (1) develop a classification model of CDM under various levels of uncertainty; (2) develop novel decision-support display concepts and evaluate them for their usefulness in supporting CDM; and (3) assess the impact of predictive analytics to mitigate uncertainty in CDM. The study of decision-making under uncertainty is needed in many high-risk systems (e.g., operators working in flight decks, submarines, firegrounds, emergency control centres, and operation rooms). In this program, we will work with the test case of operating rooms because it is representative of high-risk systems in terms of competing demands, data overload, uncertainty, short timescales, and rapidly changing conditions. As such, learnings from this program will be transferable to other contexts.