Indian Institute of Management Bangalore, India.
Iowa State University, USA.
Soc Sci Med. 2021 Jan;268:113473. doi: 10.1016/j.socscimed.2020.113473. Epub 2020 Oct 28.
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.
我们将预测偏差定义为，当根据相同的当前数据预测 x 周后的阳性 COVID 病例数量时，出现的不正确预测结果。我们的目标是研究指数增长预测偏差（EGPB）在理解 COVID-19 爆发原因方面的重要性。为此，我们的目标是记录 EGPB 在理解疾病数据中的作用，研究其在疫情发展过程中的演变，并将其与个人安全指南（如戴口罩和保持社交距离）的遵守情况联系起来。我们还研究了一种实施成本较低的行为推动是否可以显著减少 EGPB。
我们研究的科学依据是，传染病传播，尤其是在初始阶段，遵循指数函数，这意味着如果疾病具有足够的传染性，少量的阳性病例可能会迅速扩散为广泛的大流行。如果人们受到 EGPB 的影响，他们可能会对自己的感染风险做出错误的判断，从而导致对安全协议的遵守程度降低。
为了收集预测偏差的数据，我们在一个全球性的在线平台上进行了一项有激励的实验，参与者来自 43 个国家，这些国家处于 COVID-19 不同的发展阶段。我们还通过调查参与者的洗手频率和使用消毒剂和口罩的频率、他们愿意为口罩支付的金额、他们对他人行为的社会适宜性的看法、以及他们对政府应对措施的喜欢/不喜欢程度，构建了几个合规性指标。预测数据被用于构建几个 EGPB 指标。我们的实验设计使我们能够确定低估的根源是源于人们普遍低估指数过程展开速度的 EGPB。
受访者使用的疾病预测模型比实际数据生成过程明显缺乏凸性，这导致了显著的 EGPB，进而与不遵守安全措施显著负相关。与疾病发展早期的国家相比，疾病发展后期的国家的受访者的偏差要高得多。一个简单的行为推动，以原始数字而不是图表的形式展示之前的数据，会导致 EGPB 显著降低。
对疾病数据理解的行为偏差在数量上是重要的，并且是对 COVID-19 传播进行有效政策干预的严重障碍。通过原始数字清晰地传达未来的感染风险，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。
