Center for Network Big Data and Decision-Making, Business School, Sichuan University, Chengdu, China.
Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, USA.
Risk Anal. 2021 Aug;41(8):1304-1322. doi: 10.1111/risa.13626. Epub 2020 Nov 11.
In defensive resource allocation problems, the defender usually collects some forecast information about the attacker. However, the forecast information may be incorrect, which means that there could be a risk associated with the defender using it in their decision making. In this article, we propose a forecast and risk control (FRC) framework to manage the risk in defensive resource allocation with forecast information. In the FRC framework, we introduce a new measure of risk and three types of defense plans: riskless defense plan, risky defense plan, and risk-control defense plan. Several desirable properties based on the concepts of reward and penalty show that the risk-control defense plan is a general form to support defensive resource allocation. Subsequently, we study a specific defensive allocation problem with forecast information and develop an optimization model that considers the forecast information and the defender's risk tolerance level in order to obtain the risk-control defense plan with maximum reward. Furthermore, we provide some numerical analysis to illustrate the effects of forecast information and risk tolerance level on the risk-control defense plan. Finally, a numerical case study is presented to demonstrate the usability of a risk-control defense plan.
在防御性资源分配问题中,防御者通常会收集一些关于攻击者的预测信息。然而,预测信息可能是不正确的,这意味着防御者在决策中使用它可能存在风险。在本文中,我们提出了一种基于预测信息的防御性资源分配风险控制(FRC)框架来管理风险。在 FRC 框架中,我们引入了一种新的风险度量方法和三种防御计划:无风险防御计划、风险防御计划和风险控制防御计划。基于奖励和惩罚的概念,我们提出了几个理想的性质,表明风险控制防御计划是支持防御性资源分配的一般形式。随后,我们研究了一个具有预测信息的特定防御性分配问题,并开发了一个优化模型,该模型考虑了预测信息和防御者的风险容忍度水平,以获得最大奖励的风险控制防御计划。此外,我们提供了一些数值分析来说明预测信息和风险容忍度水平对风险控制防御计划的影响。最后,通过一个数值案例研究说明了风险控制防御计划的可用性。