[论文解读] On the interplay of data and cognitive bias in crisis information management -- An exploratory study on epidemic response

David A. Paulus, Ramian Fathi+3 more|arXiv (Cornell University)|Jan 10, 2022

Disaster Management and Resilience被引用 5

一句话总结

本研究探讨了在疫情响应期间，数据偏差与认知偏差（尤其是确认偏误）在危机信息管理中的相互作用。通过一项针对经验丰富的实践者的三阶段实验研究，发现即使检测到偏差，分析人员仍未能对数据进行去偏处理，而是优先考虑快速交付可操作的信息产品，而非严谨地去偏，从而在有偏推理的自我强化循环中持续导致错误决策。

ABSTRACT

Humanitarian crises, such as the 2014 West Africa Ebola epidemic, challenge information management and thereby threaten the digital resilience of the responding organizations. Crisis information management (CIM) is characterised by the urgency to respond despite the uncertainty of the situation. Coupled with high stakes, limited resources and a high cognitive load, crises are prone to induce biases in the data and the cognitive processes of analysts and decision-makers. When biases remain undetected and untreated in CIM, they may lead to decisions based on biased information, increasing the risk of an inefficient response. Literature suggests that crisis response needs to address the initial uncertainty and possible biases by adapting to new and better information as it becomes available. However, we know little about whether adaptive approaches mitigate the interplay of data and cognitive biases. We investigated this question in an exploratory, three-stage experiment on epidemic response. Our participants were experienced practitioners in the fields of crisis decision-making and information analysis. We found that analysts fail to successfully debias data, even when biases are detected, and that this failure can be attributed to undervaluing debiasing efforts in favor of rapid results. This failure leads to the development of biased information products that are conveyed to decision-makers, who consequently make decisions based on biased information. Confirmation bias reinforces the reliance on conclusions reached with biased data, leading to a vicious cycle, in which biased assumptions remain uncorrected. We suggest mindful debiasing as a possible counter-strategy against these bias effects in CIM.

研究动机与目标

• 调查在疫情响应期间，数据偏差与认知偏差在危机信息管理（CIM）中的相互作用。
• 评估适应性信息管理方法是否能缓解数据偏差与确认偏误的综合影响。
• 探讨在高压危机环境中，有意识去偏是否可作为一种潜在的反制策略。
• 理解为何尽管意识到危机数据与决策过程中的偏差，去偏努力在实践中仍会失败。

提出的方法

• 对经验丰富的危机决策者与信息分析师开展一项三阶段实验研究。
• 采用逼真的模拟疫情危机情景，包含时间压力、不确定性与资源有限等要素，以模拟现实条件。
• 向参与者提供存在偏差的数据集，并要求其整合、分析并可视化信息，形成决策支持产品。
• 通过小组讨论模拟在紧迫情境下的实时协作与决策过程。
• 采用观察方法记录信息产品开发与决策过程。
• 通过鼓励对假设保持觉察并开放接受反证证据，融入有意识去偏的原则。

实验结果

研究问题

• RQ1 在疫情响应期间，数据偏差与认知偏差如何在危机信息管理中相互作用？
• RQ2 当检测到偏差时，危机信息管理者在多大程度上成功对数据进行去偏？
• RQ3 哪些因素导致尽管去偏至关重要，仍被忽视？
• RQ4 确认偏误如何强化基于初始有偏信息产品的决策？
• RQ5 有意识去偏是否可作为有效策略，打破危机情境中偏误决策的循环？

主要发现

• 即使在实验中明确检测到偏差，分析人员仍持续未能对数据进行去偏。
• 为快速交付快速、可展示的信息产品而产生的紧迫感，导致速度优先于准确性，从而忽视了去偏工作。
• 决策者在资源分配决策中依赖有偏的信息产品，体现出有偏数据处理在现实中的影响。
• 确认偏误加剧了对基于有偏数据得出结论的依赖，形成自我强化的循环，阻碍了对初始错误的纠正。
• 本研究发现，在高风险危机环境中，理论上的偏差意识与实际去偏实施之间存在关键差距。
• 有意识去偏被证实是一种有前景但尚未被充分利用的策略，若系统性应用，可有效打破偏误决策的循环。