The influence of cognitive bias on crisis decision-making: Experimental evidence on the comparison of bias effects between crisis decision-maker groups

借记 框架效应 框架(结构) 确认偏差 认知偏差 认知 人口 信息偏差 心理学 决策者 社会心理学 经济 选择偏差 社会学 说服 工程类 管理科学 人口学 神经科学 病理 医学 结构工程
作者
David Paulus,Gerdien de Vries,Marijn Janssen,Bartel Van de Walle
出处
期刊:International journal of disaster risk reduction [Elsevier BV]
卷期号:82: 103379-103379 被引量:20
标识
DOI:10.1016/j.ijdrr.2022.103379
摘要

A crisis requires the affected population, governments or non-profit organizations, as well as crisis experts, to make urgent and sometimes life-critical decisions. With the urgency and uncertainty they create, crises are particularly amenable to inducing cognitive biases that influence decision-making. However, there is limited empirical evidence regarding the impact of cognitive biases on estimation, judgment, and decision-making tasks in crises. Possible biases occurring in crises are: (1) to be influenced by how information is framed (i.e., framing effect), (2) to overly rely on information that confirms rather than opposes preliminary assumptions (i.e., confirmation bias), (3) to rely heavily on a skewed informational cue when making estimations (i.e., anchoring bias), and (4) to see the own decision-making as less biased than decision-making of others (i.e., bias blind spot). We investigate these four cognitive biases using three online survey experiments targeting crisis-affected people of the general public (n = 460, mTurk workers), governmental and non-profit workers (n = 50, mTurk workers), and crisis experts (n = 21, purposefully sampled). Our findings show that crisis experts are the least biased group but are still significantly affected by anchoring, framing, and bias blind spot. Crisis-affected people from the general public showed the strongest susceptibility to all four biases studied. The findings have implications for future research on crisis information systems (IS) design. As crisis response is increasingly facilitated through IS, we propose debiasing functions that account for biased user behavior in crises.
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