On the Complex Quantification of Risk: Systems‐Based Perspective on Terrorism

This article highlights the complexity of the quantification of the multidimensional risk function, develops five systems‐based premises on quantifying the risk of terrorism to a threatened system, and advocates the quantification of vulnerability and resilience through the states of the system. The five premises are: (i) There exists interdependence between a specific threat to a system by terrorist networks and the states of the targeted system, as represented through the system's vulnerability, resilience, and criticality‐impact. (ii) A specific threat, its probability, its timing, the states of the targeted system, and the probability of consequences can be interdependent. (iii) The two questions in the risk assessment process: “What is the likelihood?” and “What are the consequences?” can be interdependent. (iv) Risk management policy options can reduce both the likelihood of a threat to a targeted system and the associated likelihood of consequences by changing the states (including both vulnerability and resilience) of the system. (v) The quantification of risk to a vulnerable system from a specific threat must be built on a systemic and repeatable modeling process, by recognizing that the states of the system constitute an essential step to construct quantitative metrics of the consequences based on intelligence gathering, expert evidence, and other qualitative information. The fact that the states of all systems are functions of time (among other variables) makes the time frame pivotal in each component of the process of risk assessment, management, and communication. Thus, risk to a system, caused by an initiating event (e.g., a threat) is a multidimensional function of the specific threat, its probability and time frame, the states of the system (representing vulnerability and resilience), and the probabilistic multidimensional consequences.     

Nonparametric Instrumental Regression

The focus of this paper is the nonparametric estimation of an instrumental regression function ϕ defined by conditional moment restrictions that stem from a structural econometric model E[Y−ϕ(Z)|W]=0, and involve endogenous variables Y and Z and instruments W. The function ϕ is the solution of an ill‐posed inverse problem and we propose an estimation procedure based on Tikhonov regularization. The paper analyzes identification and overidentification of this model, and presents asymptotic properties of the estimated nonparametric instrumental regression function.     

Risk Modeling, Assessment, and Management of Lahar Flow Threat

The 1991 eruption of Mount Pinatubo in the Philippines is considered one of the most violent and destructive volcanic activities in the 20th century. Lahar is the Indonesian term for volcanic ash, and lahar flows resulting from the massive amount of volcanic materials deposited on the mountain's slope posed continued post-eruption threats to the surrounding areas, destroying lives, homes, agricultural products, and infrastructures. Risks of lahar flows were identified immediately after the eruption, with scientific data provided by the Philippine Institute of Volcanology, the U.S. Geological Survey, and other research institutions. However, competing political, economic, and social agendas subordinated the importance of scientific information to policy making. Using systemic risk analysis and management, this article addresses the issues of multiple objectives and the effective integration of scientific techniques into the decision-making process. It provides a modeling framework for identifying, prioritizing, and evaluating policies for managing risk. The major considerations are: (1) applying a holistic approach to risk analysis through hierarchical holographic modeling, (2) applying statistical methods to gain insight into the problem of uncertainty in risk assessment, (3) using multiobjective trade-off analysis to address the issue of multiple decisionmakers and stakeholders in the decision-making process, (4) using the conditional expected value of extreme events to complement and supplement the expected value in quantifying risk, and (5) assessing the impacts of multistage decisions. Numerical examples based on ex post data are formulated to illustrate applications to various problems. The resulting framework from this study can serve as a general baseline model for assessing and managing risks of natural disasters, which the Philippines' lead agency-the National Disaster Coordinating Council (NDCC)-and other related organizations can use for their decision-making processes.     

Marginal methods for clustered longitudinal binary data with incomplete covariates

Many analyses for incomplete longitudinal data are directed to examining the impact of covariates on the marginal mean responses. We consider the setting in which longitudinal responses are collected from individuals nested within clusters. We discuss methods for assessing covariate effects on the mean and association parameters when covariates are incompletely observed. Weighted first and second order estimating equations are constructed to obtain consistent estimates of mean and association parameters when covariates are missing at random. Empirical studies demonstrate that estimators from the proposed method have negligible finite sample biases in moderate samples. An application to the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) demonstrates the utility of the proposed method.     

On three and four vicious walkers

We establish a reflection principle for three lattice walkers and use this principle to reduce the enumeration of configurations of three vicious walkers to the enumeration of configurations of two vicious walkers. More precisely, the reflection principle leads to a bijection between three walks (L₁, L₂, L₃) such that L₂ intersects both L₁ and L₃ and three walks (L₁, L₂, L₃) such that L₁ intersects L₃. Hence we find a combinatorial interpretation of the formula for the generating function for the number of configurations of three vicious walkers, originally derived by Bousquet-Mélou by using the kernel method, and independently by Gessel by using tableaux and symmetric functions. This answers a question posed by Gessel and Bousquet-Mélou. We also find a reflection principle for four vicious walks that leads to a combinatorial interpretation of a formula derived from Gessel's theorem.     

洛必答法则使用技巧及注意事项浅析

洛必答法则是求解极限的一个非常重要的方法,一般教材的编写,只是着重例题类型的分类专门讨论,在技巧的讨论上着墨较少,文章则从洛必答法则使用技巧以及注意事项方面针对一些实例做出浅析.     

新冠肺炎疫情突发期政策制定的特征分析与效果评价 ——基于江苏省疾病预防控制中心的田野观察

文章聚焦政策本身,采用在江苏省疾病预防控制中心进行田野观察的研究方法,收集新型冠状病毒肺炎（简称新冠肺炎）疫情突发期的政策并进行文本分析,主要讨论政策文本发布时间的分布态势、发布单位、发布形式和政策内容4个方面。研究发现,在新冠肺炎疫情突发期（2020年1月15日至1月31日）国家级和省级指导性文件数量锐增。以江苏省为例,省级政策发文单位单一、文本类型多为通知类,起到了应对突发公共卫生事件时的及时反馈、迅速应急以及布置指导的作用。政策发布主体联防联控的及时应对性以及对政策文本的长效性考虑上尚有欠缺。总结政策制定的规律、原则以及经验,提出公共卫生事件突发期的政策观念应从单一权威主义走向多元协作,政策导向应从“战”时应急转向“平战结合”,政策价值应从“短期”应急转向“常态化”指导。     

“功能性表达性模型”与中国警察信任——基于全国样本的多重中介与调节模型分析

功能性模型认为,提升警察信任的主要方法在于预防和控制犯罪,降低犯罪率与提升公众安全感;而表达性模型认为,提升警察信任更应注重维护社会正义与社区凝聚力。基于提出促进中国警察信任度提高的理论模型,提升公众获得感、幸福感、安全感,实现国家和社会整体安全的最终目的,采用结构方程模型验证“功能性-表达性模型”与中国警察信任关系的结果表明,功能性模型与表达性模型内部因素之间存在显著路径关系,同时功能性模型显著影响表达性模型指标进而改变公众对警察的信任程度。经调节效应分析后发现,主观社会阶层负向调节被害经历到犯罪恐惧感的直接路径,社交媒体使用负向调节犯罪恐惧感到警察信任的直接路径;在主观社会阶层和社交媒体使用的共同调节作用下,“被害经历→犯罪恐惧感→警察信任”中介路径效应值,随社交媒体使用频率的提升而增大,随主观社会阶层的升高而减小。     

家庭医生签约服务下社区居民就医选择的相关因素研究

为了解南京市浦口区居民就医选择现状,分析影响就医选择的相关因素,以浦口区11个社区卫生服务中心为调查地点,普查与等容抽样相结合方式纳入调查对象2301人。问卷调查收集其人口学特征、就医选择以及医疗相关行为等信息;多因素Logistic回归分析浦口区居民就医选择的相关影响因素。结果显示,调查对象中1883人(81.9%)表示可以接受以家庭医生为核心的基层首诊,1525人(66.3%)选择首先去基层医疗机构就诊。Logistic回归分析结果显示,到最近医疗机构所需时间、年龄、慢性病、是否签约家庭医生是居民接受以家庭医生为核心的基层首诊的相关因素;影响居民就医选择的相关因素则包括到最近医疗机构所需时间、年龄、文化程度、家庭人均月收入、是否签约家庭医生。浦口区居民选择基层医疗机构首诊的构成比相对较高;签约家庭医生有助于促进社区居民对基层首诊的选择与接受度;60岁及以上老年人与慢病患者对基层首诊的需求度更高,提示签约家庭医生的推广工作应重点关注这两类人群。