Choosing What to Protect

We study a strategic model in which a defender must allocate defensive resources to a collection of locations, and an attacker must choose a location to attack. The defender does not know the attacker's preferences, while the attacker observes the defender's resource allocation. The defender's problem gives rise to negative externalities, in the sense that increasing the resources allocated to one location increases the likelihood of an attack at other locations. In equilibrium, the defender exploits these externalities to manipulate the attacker's behavior, sometimes optimally leaving a location undefended, and sometimes preferring a higher vulnerability at a particular location even if a lower risk could be achieved at zero cost. Key results of our model are as follows: (1) the defender prefers to allocate resources in a centralized (rather than decentralized) manner; (2) as the number of locations to be defended grows, the defender can cost effectively reduce the probability of a successful attack only if the number of valuable targets is bounded; (3) the optimal allocation of resources can be nonmonotonic in the relative value of the attacker's outside option; and (4) the defender prefers his or her defensive allocation to be public rather than secret.     

Modeling Arbitrary Layers of Continuous‐Level Defenses in Facing with Strategic Attackers

We propose a novel class of game‐theoretic models for the optimal assignment of defensive resources in a game between a defender and an attacker. Compared to the other game‐theoretic models in the literature of defense allocation problems, the novelty of our model is that we allow the defender to assign her continuous‐level defensive resources to any subset (or arbitrary layers) of targets due to functional similarity or geographical proximity. We develop methods to solve for equilibrium, and illustrate our model using numerical examples. Compared to traditional models that only allow for individual target hardening, our results show that our model could significantly increase the defender's payoff, especially when the unit cost of defense is high.     

Resource Distribution in Multiple Attacks Against a Single Target

A target is protected by the defender and attacked by an attacker launching sequential attacks. For each attack, a contest intensity measures whether the agents’ efforts have low or high impact on the target vulnerability (low vs. high contest intensity). Both the defender and the attacker have limited resources. It is assumed that the attacker can observe the outcome of each attack and stop the sequence of attacks when the target is destroyed. Two attacker objectives are considered, that is, to maximize the target vulnerability or to minimize the expected attacker resource expenditure. The article addresses the following three questions: whether the attacker should allocate its entire resource into one large attack or distribute it among several attacks; whether geometrically increasing or decreasing resource distribution into a fixed number of sequential attacks is more beneficial than equal resource distribution; and how the optimal attack strategy depends on the contest intensity.     

An Attacker–defender Resource Allocation Game with Substitution and Complementary Effects

The United States is funding homeland security programs with a large budget (e.g., 74.4 billion for FY 2019). A number of game-theoretic defender–attacker models have been developed to study the optimal defense resource allocation strategies for the government (defender) against the strategic adversary (attacker). However, to the best of our knowledge, the substitution or complementary effects between different types of defensive resources (e.g., human resource, land resource, and capital resource) have not been taken into consideration even though they exist in practice. The article fills this gap by studying a sequential game-theoretical resource allocation model and then exploring how the joint effectiveness of multiple security investments influences the defensive budget allocation among multiple potential targets. Three false belief models have been developed in which only the defender, only the attacker, and both the defender and attacker hold false beliefs about the joint effectiveness of resources. Regression analysis shows that there are significant substitution effects between human and capital resources. The results show that the defender will suffer a higher loss if he fails to consider the substitution or complementary effects. Interestingly, if the attacker holds a false belief while the defender does not, the defender will suffer an even higher loss, especially when the resources are substitutes. However, if both the attacker and defender hold false beliefs, there will be lower loss when resources are complementary. The results also show that the defender should allocate the highly effective resource when the resources substitute each other. This article provides some new insights to the homeland security resource allocation.     

A Comparative Analysis of PRA and Intelligent Adversary Methods for Counterterrorism Risk Management

In counterterrorism risk management decisions, the analyst can choose to represent terrorist decisions as defender uncertainties or as attacker decisions. We perform a comparative analysis of probabilistic risk analysis (PRA) methods including event trees, influence diagrams, Bayesian networks, decision trees, game theory, and combined methods on the same illustrative examples (container screening for radiological materials) to get insights into the significant differences in assumptions and results. A key tenent of PRA and decision analysis is the use of subjective probability to assess the likelihood of possible outcomes. For each technique, we compare the assumptions, probability assessment requirements, risk levels, and potential insights for risk managers. We find that assessing the distribution of potential attacker decisions is a complex judgment task, particularly considering the adaptation of the attacker to defender decisions. Intelligent adversary risk analysis and adversarial risk analysis are extensions of decision analysis and sequential game theory that help to decompose such judgments. These techniques explicitly show the adaptation of the attacker and the resulting shift in risk based on defender decisions.     

Adversarial risk analysis for counterterrorism modeling

Recent large-scale terrorist attacks have raised interest in models for resource allocation against terrorist threats. The unifying theme in this area is the need to develop methods for the analysis of allocation decisions when risks stem from the intentional actions of intelligent adversaries. Most approaches to these problems have a game-theoretic flavor although there are also several interesting decision-analytic-based proposals. One of them is the recently introduced framework for adversarial risk analysis, which deals with decision-making problems that involve intelligent opponents and uncertain outcomes. We explore how adversarial risk analysis addresses some standard counterterrorism models: simultaneous defend-attack models, sequential defend-attack-defend models, and sequential defend-attack models with private information. For each model, we first assess critically what would be a typical game-theoretic approach and then provide the corresponding solution proposed by the adversarial risk analysis framework, emphasizing how to coherently assess a predictive probability model of the adversary's actions, in a context in which we aim at supporting decisions of a defender versus an attacker. This illustrates the application of adversarial risk analysis to basic counterterrorism models that may be used as basic building blocks for more complex risk analysis of counterterrorism problems.     

一类网上英式拍卖：顾客投标行为研究

网上拍卖作为一种新型营销手段正在越演越烈,在电子商务中举足轻重.网上拍卖结合传统拍卖与IT技术,有其独特性,文章以eBay为背景,研究一类有代理的网上英式拍卖,其中顾客逐个到达,卖方可以设置保留价也可以不设置保留价.具体分析了此类网上拍卖的动态进程.基此,在对顾客的到达过程与估价分布函数不作具体假设的一般情形下,证明了对于个人价值独立模型,顾客一到达就报价且报实价为占优战略.进而,将此与文献中关于关联价值模型下的均衡投标策略进行了比较分析,并解释了网上拍卖实践中的一些现象.     

Hand-Hygiene Mitigation Strategies Against Global Disease Spreading through the Air Transportation Network

The risk for a global transmission of flu-type viruses is strengthened by the physical contact between humans and accelerated through individual mobility patterns. The Air Transportation System plays a critical role in such transmissions because it is responsible for fast and long-range human travel, while its building components—the airports—are crowded, confined areas with usually poor hygiene. Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO) consider hand hygiene as the most efficient and cost-effective way to limit disease propagation. Results from clinical studies reveal the effect of hand washing on individual transmissibility of infectious diseases. However, its potential as a mitigation strategy against the global risk for a pandemic has not been fully explored. Here, we use epidemiological modeling and data-driven simulations to elucidate the role of individual engagement with hand hygiene inside airports in conjunction with human travel on the global spread of epidemics. We find that, by increasing travelers engagement with hand hygiene at all airports, a potential pandemic can be inhibited by 24% to 69%. In addition, we identify 10 airports at the core of a cost-optimal deployment of the hand-washing mitigation strategy. Increasing hand-washing rate at only those 10 influential locations, the risk of a pandemic could potentially drop by up to 37%. Our results provide evidence for the effectiveness of hand hygiene in airports on the global spread of infections that could shape the way public-health policy is implemented with respect to the overall objective of mitigating potential population health crises.     

Efficient Allocation of Resources for Defense of Spatially Distributed Networks Using Agent‐Based Simulation

This article presents ongoing research that focuses on efficient allocation of defense resources to minimize the damage inflicted on a spatially distributed physical network such as a pipeline, water system, or power distribution system from an attack by an active adversary, recognizing the fundamental difference between preparing for natural disasters such as hurricanes, earthquakes, or even accidental systems failures and the problem of allocating resources to defend against an opponent who is aware of, and anticipating, the defender's efforts to mitigate the threat. Our approach is to utilize a combination of integer programming and agent‐based modeling to allocate the defensive resources. We conceptualize the problem as a Stackelberg “leader follower” game where the defender first places his assets to defend key areas of the network, and the attacker then seeks to inflict the maximum damage possible within the constraints of resources and network structure. The criticality of arcs in the network is estimated by a deterministic network interdiction formulation, which then informs an evolutionary agent‐based simulation. The evolutionary agent‐based simulation is used to determine the allocation of resources for attackers and defenders that results in evolutionary stable strategies, where actions by either side alone cannot increase its share of victories. We demonstrate these techniques on an example network, comparing the evolutionary agent‐based results to a more traditional, probabilistic risk analysis (PRA) approach. Our results show that the agent‐based approach results in a greater percentage of defender victories than does the PRA‐based approach.     

Living beyond the unanticipated sudden death of a partner: a phenomenological study

This research project explored grief and its impact upon men and women who have experienced the sudden and unanticipated death of his or her partner. It included what grief meant to them, how it was manifested in his or her everyday lives and how his or her partner's death had impacted upon his or her relationship with themselves, with others and the world. A Husserlian phenomenological approach was used to explore the experiences of the ten women and five men whose partner had died up to five years prior to being interviewed. The need for the surviving partner to continue to participate in everyday life placed great strain upon the internal resources of the surviving partner. The surviving partner needed to reinvent him or herself, in an attempt to become independent and regain functionality, whilst dealing with the sadness and loss that they had experienced. The surviving partner discovered that a new life order emerged that included hope, optimism, planning for the future and perhaps the prospect of a new relationship. The death of a partner left the surviving partner with a loss that would always be a part of them, with the memories of his or her relationship being maintained within them that will never be replaced by somebody else. The results of this research project reinforce the need for ongoing education of the community in grief and bereavement issues in order to increase the awareness of the support needs of the bereaved person. The length of time and amount of energy required to incorporate the experience into the survivor's life, is greatly underestimated by the community, and perhaps by some of the health and caring professionals. Colonial and hospital based bereavement support services need to be established and be proactive using outreach programs, actively offering the suddenly bereaved partner and family support and information.     

Some Limitations of “Risk = Threat × Vulnerability × Consequence” for Risk Analysis of Terrorist Attacks

Several important risk analysis methods now used in setting priorities for protecting U.S. infrastructures against terrorist attacks are based on the formula: Risk=Threat×Vulnerability×Consequence. This article identifies potential limitations in such methods that can undermine their ability to guide resource allocations to effectively optimize risk reductions. After considering specific examples for the Risk Analysis and Management for Critical Asset Protection (RAMCAP?) framework used by the Department of Homeland Security, we address more fundamental limitations of the product formula. These include its failure to adjust for correlations among its components, nonadditivity of risks estimated using the formula, inability to use risk‐scoring results to optimally allocate defensive resources, and intrinsic subjectivity and ambiguity of Threat, Vulnerability, and Consequence numbers. Trying to directly assess probabilities for the actions of intelligent antagonists instead of modeling how they adaptively pursue their goals in light of available information and experience can produce ambiguous or mistaken risk estimates. Recent work demonstrates that two‐level (or few‐level) hierarchical optimization models can provide a useful alternative to Risk=Threat×Vulnerability×Consequence scoring rules, and also to probabilistic risk assessment (PRA) techniques that ignore rational planning and adaptation. In such two‐level optimization models, defender predicts attacker's best response to defender's own actions, and then chooses his or her own actions taking into account these best responses. Such models appear valuable as practical approaches to antiterrorism risk analysis.     

Optimal Resource Allocation for Defense of Targets Based on Differing Measures of Attractiveness

This article describes the results of applying a rigorous computational model to the problem of the optimal defensive resource allocation among potential terrorist targets. In particular, our study explores how the optimal budget allocation depends on the cost effectiveness of security investments, the defender's valuations of the various targets, and the extent of the defender's uncertainty about the attacker's target valuations. We use expected property damage, expected fatalities, and two metrics of critical infrastructure (airports and bridges) as our measures of target attractiveness. Our results show that the cost effectiveness of security investment has a large impact on the optimal budget allocation. Also, different measures of target attractiveness yield different optimal budget allocations, emphasizing the importance of developing more realistic terrorist objective functions for use in budget allocation decisions for homeland security.     

Game Theory and Risk Analysis

Risk analysts often analyze adversarial risks from terrorists or other intelligent attackers without mentioning game theory. Why? One reason is that many adversarial situations—those that can be represented as attacker‐defender games, in which the defender first chooses an allocation of defensive resources to protect potential targets, and the attacker, knowing what the defender has done, then decides which targets to attack—can be modeled and analyzed successfully without using most of the concepts and terminology of game theory. However, risk analysis and game theory are also deeply complementary. Game‐theoretic analyses of conflicts require modeling the probable consequences of each choice of strategies by the players and assessing the expected utilities of these probable consequences. Decision and risk analysis methods are well suited to accomplish these tasks. Conversely, game‐theoretic formulations of attack‐defense conflicts (and other adversarial risks) can greatly improve upon some current risk analyses that attempt to model attacker decisions as random variables or uncertain attributes of targets (“threats”) and that seek to elicit their values from the defender's own experts. Game theory models that clarify the nature of the interacting decisions made by attackers and defenders and that distinguish clearly between strategic choices (decision nodes in a game tree) and random variables (chance nodes, not controlled by either attacker or defender) can produce more sensible and effective risk management recommendations for allocating defensive resources than current risk scoring models. Thus, risk analysis and game theory are (or should be) mutually reinforcing.     

Reasons for Secrecy and Deception in Homeland‐Security Resource Allocation

In this article, we explore reasons that a defender might prefer secrecy or deception about her defensive resource allocations, rather than disclosure, in a homeland‐security context. Our observations not only summarize and synthesize the results of existing game‐theoretic work, but also provide intuitions about promising future research directions.     

Is a disease leader attractive? Six tests of whether the COVID-19 pandemic affected follower preferences for attractiveness,health and other traits in political and non-political leaders

Attractive political candidates receive more votes on Election Day compared to their less attractive competitors. One well-cited theoretical account for this attractiveness effect (White et al., 2013) holds that it reflects an adaptive psychological response to disease threats. Voters are predicted to upregulate preferences for attractiveness because it constitutes a cue to health. The global COVID-19 pandemic constitutes an ecologically relevant and realistic setting for further testing this prediction. Here, we report the results from six tests of the prediction based on two large and nationally representative surveys conducted in Denmark (n = 3297) at the outbreak of the pandemic and one year later. Utilizing experimental techniques, validated individual difference measures of perceived disease threat and geographic data on COVID-19 severity, we do not find that disease threats like the COVID-19 pandemic upregulate preferences for attractive and healthy political or non-political leaders. Instead, respondents display heightened preferences for health in socially proximate relations (i.e. colleagues). Moreover, individuals who react aversively to situations involving risks of pathogen transmission (scoring high in Germ Aversion) report higher importance of a wide range of leadership traits, rather than for health and attractiveness in particular. Results are discussed in relation to evolutionary accounts of leadership and followership.     

Providing health care to human beings trapped in the poverty culture

The culture of poverty impacts everything patients in this socioeconomic group think and do. If what poor patients say does not sit well with the way we think, that doesn't mean they are wrong. Physicians have to adjust their mental model and think in different cultural terms. The author recently completed his thirtieth year of a career dedicated to providing health care to people living in poverty. He shares seven concepts important in building a mental model that will enable physicians to successfully provide health care to this patient population: (1) Poverty is the number one health problem; (2) we see same diseases as everyone else; (3) patients are trapped in the poverty culture; (4) patients' behavior is often manipulative; (5) compliance is a unique challenge; (6) patients have limited resources; and (7) the ultimate contributors to poverty are unwanted adolescent pregnancy and substance abuse. These concepts can help physicians to be more effective in providing health care to patients living in poverty. They can help them understand what is happening, so that their experience might be fulfilling rather than demoralizing.     

全球视角下跨境医疗服务研究述评与展望

本文从跨境医疗研究现状出发,对跨境医疗新趋势进行分析。研究发现关于跨境医疗的研究成果较多基于传统的医疗管理和公共卫生理论与模式。互联网时代跨境医疗的特征和医疗服务模式发生了变革,在这一背景下的相关研究较少。本文系统分析了跨境医疗整体概况及测度研究、驱动机制与服务模式研究成果,有针对性地提炼跨境医疗研究的理论框架,探讨跨境医疗带来的全球医疗资源均等化配置问题,提出新趋势下跨境医疗研究的关键科学问题与相关研究建议。研究成果对于推动国内外学者开展跨境医疗前沿领域研究,贡献全球健康事业有着重要的理论价值和实践意义。     

Total Economic Consequences of an Influenza Outbreak in the United States

Pandemic influenza represents a serious threat not only to the population of the United States, but also to its economy. In this study, we analyze the total economic consequences of potential influenza outbreaks in the United States for four cases based on the distinctions between disease severity and the presence/absence of vaccinations. The analysis is based on data and parameters on influenza obtained from the Centers for Disease Control and the general literature. A state‐of‐the‐art economic impact modeling approach, computable general equilibrium, is applied to analyze a wide range of potential impacts stemming from the outbreaks. This study examines the economic impacts from changes in medical expenditures and workforce participation, and also takes into consideration different types of avoidance behavior and resilience actions not previously fully studied. Our results indicate that, in the absence of avoidance and resilience effects, a pandemic influenza outbreak could result in a loss in U.S. GDP of $25.4 billion, but that vaccination could reduce the losses to $19.9 billion. When behavioral and resilience factors are taken into account, a pandemic influenza outbreak could result in GDP losses of $45.3 billion without vaccination and $34.4 billion with vaccination. These results indicate the importance of including a broader set of causal factors to achieve more accurate estimates of the total economic impacts of not just pandemic influenza but biothreats in general. The results also highlight a number of actionable items that government policymakers and public health officials can use to help reduce potential economic losses from the outbreaks.     

基于两阶段资源分配协调机制的分布式多项目随机调度

企业趋向于多项目共享全局资源的分布式协同管理。但在多项目实际执行时，全局资源可用量往往由于外部环境的动态变化而存在不确定性，活动中断、资源浪费等现象频发，项目管理变得愈加复杂。本文将不确定的全局资源可用量建模为随机变量，设计两阶段资源分配协调机制，在预分配阶段，考虑项目允许的最大活动中断次数约束，建立各项目调度的马尔可夫动态决策过程模型；预分配结束后，基于活动重要度依次对剩余全局资源进行协调再分配，以提高资源利用率并减少平均项目延期。设计基于全局资源协调分配的Rollout近似动态规划算法进行求解。开展问题库算例实验研究与案例分析，验证协调机制与求解算法的性能；同时，探讨并分析不确定参数对目标结果的影响。     

Pandemic refuges: Lessons from 2 years of COVID-19

This paper relates evidence from the COVID-19 pandemic to the concept of pandemic refuges, as developed in literature on global catastrophic risk. In this literature, a refuge is a place or facility designed to keep a portion of the population alive during extreme global catastrophes. COVID-19 is not the most extreme pandemic scenario, but it is nonetheless a very severe global event, and it therefore provides an important source of evidence. Through the first 2 years of the COVID-19 pandemic, several political jurisdictions have achieved low spread of COVID-19 via isolation from the rest of the world and can therefore classify as pandemic refuges. Their suppression and elimination of COVID-19 demonstrates the viability of pandemic refuges as a risk management measure. Whereas prior research emphasizes island nations as pandemic refuges, this paper uses case studies of China and Western Australia to show that other types of jurisdictions can also successfully function as pandemic refuges. The paper also refines the concept of pandemic refuges and discusses implications for future pandemics.