Designing Tools to Support Cognitive Decision Making Under Uncertainty

高风险行业（例如，航空、海军、核能、医疗保健）由于操作人员与其决策支持工具之间的交互所带来的复杂性而易受安全故障的影响。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合作，以便在出现不确定性时进行检测和适当管理。了解如何在不确定性期间支持操作员的认知决策（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。* 当前的决策支持工具不完全支持操作员决策。操作员继续经历认知过载、误解显示和不信任信号，从而导致不准确的决策。自动化可以有效地用于定义明确的情况，其中了解有关最佳实践的知识和证据。然而，在现实中，运营商经常面临着不断变化的情况，他们必须适应，自动化的方法不再适合，因为他们设计的预期环境已经发生了变化。操作员必须根据不确定性的水平在自动化和自适应CDM之间切换，这可能源于情况（例如，时间意识的损失），环境因素（例如，功率损失）和操作员的技能/知识。当正常的节奏被打乱时，不确定性就会突然出现，范围从平凡到灾难性的事件。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采用自动化和自适应CDM。我们如何控制不必要的变化，如何识别、重视和支持必要的变化？这是一个复杂的、多层面的问题，也是一个非常重要的领域，但基本上被忽视了。** 本研究计划的长期目标是通过引入决策支持工具来优化CDM质量，这些工具利用预测分析来检测并向运营商传达不确定性。短期目标是：（1）在各种不确定性水平下开发清洁发展机制的分类模型;（2）开发新的决策支持显示概念，并评估其在支持清洁发展机制方面的有用性;（3）评估预测分析对减轻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、消防站、应急控制中心和操作室工作的操作员）。在这个项目中，我们将使用手术室的测试案例，因为它在竞争需求，数据过载，不确定性，短时间尺度和快速变化的条件方面代表了高风险系统。因此，从该计划中学习将转移到其他环境。*****

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. *****