Designing Tools to Support Cognitive Decision Making Under Uncertainty

高风险行业(如航空、海军、核电、医疗保健)很容易受到安全故障的影响，因为操作员与其决策支持工具之间的交互作用造成了复杂性。不确定性带来了额外的复杂性，因为运营商必须与决策支持工具协作，以便在不确定性出现时检测并适当地管理它。了解如何在不确定性期间支持运营商的认知决策(CDM)对于设计决策支持工具以在正确的时间产生正确的行为至关重要。目前的决策支持工具并不完全支持运营商的决策。操作员继续经历认知过载、曲解显示和不信任信号，导致决策不准确。自动化对于明确定义的情况是有效的，在这种情况下，围绕最佳实践的知识和证据得到了理解。然而，在现实中，运营商经常面临他们必须适应的不断变化的情况，在这种情况下，自动化方法不再适合，因为它们设计时所针对的预期环境已经改变。操作员必须根据不确定性的程度在自动化和自适应清洁发展机制之间切换，这种不确定性可能源于情况(例如，失去时间意识)、环境因素(例如，失去动力)以及操作员的技能/知识。当通常的节奏被打乱时，不确定性就会随之而来，范围从平凡的事件到灾难性的事件。我们必须设计工具来支持运营商在管理安全风险时有效地利用自动化和适应性清洁发展机制。我们如何控制不必要的变化，并识别、评价和支持必要的变化？这是一个复杂的、多层面的问题，也是一个基本上被忽视的极其重要的领域。该研究计划的长期目标是通过引入决策支持工具来优化清洁发展机制的质量，这些工具利用预测性分析来检测不确定性并将其传达给运营商。短期目标是：(1)开发不同不确定性水平下的清洁发展机制分类模型；(2)开发新的决策支持展示概念，并评估它们在支持清洁发展机制方面的有用性；(3)评估预测性分析对减少清洁发展机制不确定性的影响。在许多高风险系统(例如，在飞行甲板、潜艇、火炮、紧急控制中心和操作室工作的操作员)中，需要研究不确定情况下的决策。在本计划中，我们将使用手术室的测试用例，因为它在竞争需求、数据过载、不确定性、短时间范围和快速变化的条件方面代表了高风险系统。因此，从该计划中学到的内容将可移植到其他环境中。

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.