幸福的无知:流行病中信息的价值

Keyvan Eslami, H. Lee
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引用次数: 0

摘要

本文研究了COVID-19大流行期间有关真实感染人数的部分信息如何影响最佳缓解和检测政策。首先,我们记录了突出信息价值的两个鼓舞人心的观察结果:首先,与大流行后期相比,大流行初期在缓解方面反应过度;第二,我们称之为“幸福无知”的趋势,即较少的测试与较少的缓解措施相关。我们表明,这些可以通过部分信息下的最优策略来证明。具体来说,我们开发了一个流行病学模型,其中感染的真实人数可以部分地从两个信号推断:住院和检测。一个平等主义的计划者可以决定缓解和测试的程度,这影响到感染率和关于感染者的信号噪音。使用校准模型,我们的主要结果表明,如果充分执行缓解措施,计划者愿意放弃17%的产出用于测试以消除不确定性。如果不进行测试,在缓解方面高达35%的过度反应可以部分取代测试的这种信息作用。最后,当缓解无法强制执行时,计划人员可以通过减少测试的数量来保持最佳状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Blissful Ignorance: the Value of Information in a Pandemic
This paper studies how partial information regarding the true number of infected affects optimal mitigation and testing policies during the COVID-19 pandemic. We start by documenting two motivating observations which highlight the value of information: First, an overreaction in mitigation at the onset of the pandemic compared to its later stages; Second, a tendency for what we call "blissful ignorance," where less testing is associated with fewer mitigation measures in place. We show that these can be justified through the lens of optimal policies under partial information. Specifically, we develop an epidemiological model where the true number of infected can be partially inferred from two signals: hospitalization and testing. An egalitarian planner can decide on the degree of mitigation and testing, which affect infection rates and signal noises about the infected. Using the calibrated model, our main results show that the planner is willing to give up 17% of output for testing to eliminate the uncertainty, provided the full enforcement of mitigation. Absent testing, an overreaction of up to 35% in mitigation can partially replace this information role of testing. Finally, when mitigation is not enforceable, the planner optimally remains blissfully ignorant by reducing the number of tests.
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