危机信息管理中数据与认知偏差的相互作用--以疫情应对为例
On the interplay of data and cognitive bias in crisis information management -- An exploratory study on epidemic response
摘要：
2014年西非埃博拉疫情等人道主义危机对信息管理提出了挑战，从而威胁到应对组织的数字复原力。危机信息管理（CIM）的特点是，尽管情况不确定，但仍需作出紧急反应。再加上风险高、资源有限和认知负荷大，危机很容易导致分析人员和决策者在数据和认知过程中产生偏差。当CIM中的偏见未被发现和处理时，它们可能会导致基于有偏见的信息做出决策，从而增加响应效率低下的风险。文献表明，危机应对需要通过适应新的和更好的信息来解决最初的不确定性和可能的偏差。然而，我们对适应性方法是否能减轻数据和认知偏差的相互作用知之甚少。我们在一个关于流行病应对的探索性三阶段实验中调查了这个问题。我们的参与者是危机决策和信息分析领域的经验丰富的从业人员。我们发现，分析师未能成功地去偏置数据，即使偏见被检测到，这种失败可以归因于低估去偏置的努力，有利于快速的结果。这一失败导致了有偏见的信息产品的发展，这些产品被传达给决策者，他们因此根据有偏见的信息做出决定。确认偏差强化了对有偏见数据得出的结论的依赖，导致恶性循环，其中有偏见的假设仍然没有得到纠正。我们建议有意识的去偏置作为一种可能的反策略，对这些偏见的影响，在CIM。
英文摘要：
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.
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