Luo Jianxi
Data-Driven Innovation Lab, Singapore University of Technology & Design (SUTD), 8 Somapah Road, 487372, Singapore.
Technol Forecast Soc Change. 2021 May;166:120602. doi: 10.1016/j.techfore.2021.120602. Epub 2021 Jan 19.
During the current COVID-19 pandemic, there have been many efforts to forecast infection cases, deaths, and courses of development, using a variety of mechanistic, statistical, or time-series models. Some forecasts have influenced policies in some countries. However, forecasting future developments in the pandemic is fundamentally challenged by the innate uncertainty rooted in many "unknown unknowns," not just about the contagious virus itself but also about the intertwined human, social, and political factors, which co-evolve and keep the future of the pandemic open-ended. These unknown unknowns make the accuracy-oriented forecasting misleading. To address the extreme uncertainty of the pandemic, a heuristic approach and exploratory mindset is needed. Herein, grounded on our own COVID-19 forecasting experiences, I propose and advocate the "" paradigm, which synthesizes prediction and monitoring, to make government policies, organization planning, and individual mentality heuristically future-informed despite the extreme uncertainty.
在当前的新冠疫情大流行期间，人们做出了许多努力，运用各种机理模型、统计模型或时间序列模型来预测感染病例、死亡人数和疫情发展进程。一些预测对某些国家的政策产生了影响。然而，预测疫情的未来发展面临着根本性的挑战，这种挑战源于许多“未知的未知”所固有的不确定性，这不仅涉及传染性病毒本身，还涉及相互交织的人类、社会和政治因素，这些因素共同演变，使得疫情的未来具有开放性。这些未知的未知使得以准确性为导向的预测具有误导性。为应对疫情的极端不确定性，需要一种启发式方法和探索性思维。在此，基于我们自己的新冠疫情预测经验，我提出并倡导“ ”范式，该范式将预测与监测相结合，以使政府政策、组织规划和个人心态在极端不确定性的情况下，能以启发式的方式为未来提供参考。 （注：原文中“ ”处内容缺失）
