During an epidemic, the information available to individuals in the society deeply influences their belief of the epidemic spread, and consequently the preventive measures they take to stay safe from the infection. In this paper, we develop a scalable framework for ascertaining the optimal information disclosure a government must make to individuals in a networked society for the purpose of epidemic containment. This problem of information design problem is complicated by the heterogeneous nature of the society, the positive externalities faced by individuals, and the variety in the public response to such disclosures. We use a networked public goods model to capture the underlying societal structure. Our first main result is a structural decomposition of the government's objectives into two independent components -- a component dependent on the utility function of individuals, and another dependent on properties of the underlying network. Since the network dependent term in this decomposition is unaffected by the signals sent by the government, this characterization simplifies the problem of finding the optimal information disclosure policies. We find explicit conditions, in terms of the risk aversion and prudence, under which no disclosure, full disclosure, exaggeration and downplay are the optimal policies. The structural decomposition results are also helpful in studying other forms of interventions like incentive design and network design.
翻译:在流行病期间,社会上个人掌握的信息深刻地影响他们对流行病传播的信念,进而影响他们为避免感染而采取的预防措施。在本文件中,我们制定了一个可扩展的框架,以确定政府为了遏制流行病,必须向网络社会的个人披露最佳信息;信息设计问题由于社会的多样性、个人面临的积极外差因素以及公众对这种披露的反应的多样性而变得更加复杂。我们使用网络化的公益模式来捕捉基本的社会结构。我们的第一个主要结果是将政府的目标分为两个独立的部分 -- -- 一个部分取决于个人的实用功能,另一个部分取决于基本网络的特性。由于这一分解的网络的术语不受政府发出的信号的影响,这种定性简化了寻找最佳信息披露政策的问题。我们从风险规避和审慎的角度发现明确的条件,根据这种条件,没有披露、充分披露、夸张和淡化是最佳政策。结构分解结果也有助于研究其他形式的干预设计,例如激励性网络的设计。