Center for Network Big Data and Decision-Making, Business School, Sichuan University, Chengdu, China.
Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, New York, USA.
Risk Anal. 2023 Jun;43(6):1235-1253. doi: 10.1111/risa.13992. Epub 2022 Jul 15.
The outbreak of pandemics such as COVID-19 can result in cascading effects for global systemic risk. To combat an ongoing pandemic, governmental resources are largely allocated toward supporting the health of the public and economy. This shift in attention can lead to security vulnerabilities which are exploited by terrorists. In view of this, counterterrorism during a pandemic is of critical interest to the safety and well-being of the global society. Most notably, the population flows among potential targets are likely to change in conjunction with the trend of the health crisis, which leads to fluctuations in target valuations. In this situation, a new challenge for the defender is to optimally allocate his/her resources among targets that have changing valuations, where his/her intention is to minimize the expected losses from potential terrorist attacks. In order to deal with this challenge, in this paper, we first develop a defender-attacker game in sequential form, where the target valuations can change as a result of the pandemic. Then we analyze the effects of a pandemic on counterterrorism resource allocation from the perspective of dynamic target valuations. Finally, we provide some examples to display the theoretical results, and present a case study to illustrate the usability of our proposed model during a pandemic.
COVID-19 等大流行病的爆发会对全球系统性风险产生连锁反应。为了应对正在发生的大流行病，政府资源在很大程度上被分配用于支持公众和经济的健康。这种注意力的转移可能会导致安全漏洞，从而被恐怖分子利用。有鉴于此，大流行病期间的反恐对于全球社会的安全和福祉至关重要。值得注意的是，潜在目标之间的人口流动可能会随着健康危机的趋势而变化，这导致目标估值的波动。在这种情况下，防御者面临的一个新挑战是在具有变化估值的目标之间优化分配资源，其意图是将潜在恐怖袭击的预期损失降到最低。为了应对这一挑战，本文首先在序贯形式下建立了一个防御者-攻击者博弈，其中目标估值会因大流行病而发生变化。然后，我们从动态目标估值的角度分析大流行病对反恐资源分配的影响。最后，我们提供了一些例子来展示理论结果，并进行了案例研究，以说明我们在大流行期间提出的模型的可用性。
