Exponential-growth prediction bias and compliance with safety measures related to COVID-19.
Exponential-growth prediction bias and compliance with safety measures related to COVID-19.
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DOI：
10.1016/j.socscimed.2020.113473
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发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Majumdar P
中科院分区：
文献类型：
--
作者：
Banerjee R;Bhattacharya J;Majumdar P
我们将预测偏差定义为系统误差，该系统误差是由于对阳性COVID病例数量的错误预测而产生的，因此，当与y周之前的实际数据一起呈现时，X周。我们的目标是研究指数增长预测偏差（EGPB）在理解COVID-19爆发爆发原因方面的重要性。为此，我们的目标是记录EGPB在疾病数据的理解，研究它如何随着流行病的进展而演变，并将其与个人安全准则的遵守情况联系起来，例如使用面部覆盖物和社交距离。我们还调查了是否行为轻推，成本较低的实施，可以显着降低EGPB。我们调查的科学基础是公认的智慧，即传染病的传播，特别是在最初阶段，遵循指数函数，这意味着如果疾病具有足够的传播性，很少有阳性病例可以爆发成大范围的流行病。如果人们患有EGPB，他们可能会对自己的感染风险做出错误的判断，这反过来可能会导致安全协议的合规性降低。为了收集预测偏差的数据，我们在一个全球在线平台上进行了一项激励实验，参与者来自43个国家，每个国家都处于COVID-19的不同进展阶段。我们还通过调查参与者洗手和使用消毒剂和口罩的频率，他们购买口罩的意愿，他们对他人行为的社会适当性的看法，以及他们对政府反应的喜欢/不喜欢，构建了几个合规指数。预测数据被用来构建EGPB的几个措施。我们的实验设计使我们能够识别预测不足的根源，因为EGPB产生于低估指数过程展开速度的一般趋势。受访者使用比实际数据生成过程凸性小得多的模型来预测疾病的传播路径。这就产生了显著的EGPB，而EGPB又与不遵守安全措施显著负相关。相对于处于疾病进展早期阶段的国家，处于疾病进展后期阶段的国家的受访者的偏倚显著更高。一个简单的行为轻推，显示原始数据方面的先验数据，而不是一个图形，因果关系减少EGPB。有关理解疾病数据的行为偏差在数量上很重要，严重阻碍了针对COVID-19传播的有效政策行动。通过原始数据明确传达未来感染风险可以提高风险感知的准确性，从而促进对建议的保护行为的遵守。
We define prediction bias as the systematic error arising from an incorrect prediction of the number of positive COVID cases x-weeks hence when presented with y-weeks of prior, actual data on the same. Our objective is to investigate the importance of an exponential-growth prediction bias (EGPB) in understanding why the COVID-19 outbreak has exploded. To that end, our goal is to document EGPB in the comprehension of disease data, study how it evolves as the epidemic progresses, and connect it with compliance of personal safety guidelines such as the use of face coverings and social distancing. We also investigate whether a behavioral nudge, cost less to implement, can significantly reduce EGPB. The scientific basis for our inquiry is the received wisdom that infectious disease spread, especially in the initial stages, follows an exponential function meaning few positive cases can explode into a widespread pandemic if the disease is sufficiently transmittable. If people suffer from EGPB, they will likely make incorrect judgments about their infection risk, which in turn, may lead to reduced compliance of safety protocols. To collect data on prediction bias, we ran an incentivized, experiment on a global, online platform with participation from people in forty-three countries, each at different stages of progression of COVID-19. We also constructed several indices of compliance by surveying participants about their frequency of hand-washing and use of sanitizers and masks; their willingness to pay for masks; their view about the social appropriateness of others’ behavior; and their like/dislike of government responses. The prediction data was used to construct several measures of EGPB. Our experimental design permits us to identify the root of under-prediction as EGPB arising from the general tendency to underestimate the speed at which exponential processes unfold. Respondents make predictions about the path of the disease using a model that is substantially less convex than the actual data generating process. This creates significant EGPB, which, in turn, is significantly and negatively associated with non-compliance with safety measures. The bias is significantly higher for respondents from countries at a later stage relative to those at an early stage of disease progression. A simple behavioral nudge that shows prior data in terms of raw numbers, as opposed to a graph, causally reduces EGPB. Behavioral biases concerning the comprehension of disease data are quantitatively important, and act as severe impediments to effective policy action against the spread of COVID-19. Clear communication of future infection risk via raw numbers could increase the accuracy of risk perception, in turn, facilitating compliance with suggested protective behaviors.
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