An Agent‐Based Model of Evolving Community Flood Risk

Although individual behavior plays a major role in community flood risk, traditional flood risk models generally do not capture information on how community policies and individual decisions impact the evolution of flood risk over time. The purpose of this study is to improve the understanding of the temporal aspects of flood risk through a combined analysis of the behavioral, engineering, and physical hazard aspects of flood risk. Additionally, the study aims to develop a new modeling approach for integrating behavior, policy, flood hazards, and engineering interventions. An agent‐based model (ABM) is used to analyze the influence of flood protection measures, individual behavior, and the occurrence of floods and near‐miss flood events on community flood risk. The ABM focuses on the following decisions and behaviors: dissemination of flood management information, installation of community flood protection, elevation of household mechanical equipment, and elevation of homes. The approach is place based, with a case study area in Fargo, North Dakota, but is focused on generalizable insights. Generally, community mitigation results in reduced future damage, and individual action, including mitigation and movement into and out of high‐risk areas, can have a significant influence on community flood risk. The results of this study provide useful insights into the interplay between individual and community actions and how it affects the evolution of flood risk. This study lends insight into priorities for future work, including the development of more in‐depth behavioral and decision rules at the individual and community level.     

印度空间碎片减缓国家机制述评及其启示

2007年6月,联合国外空委在其通过的《空间碎片减缓指南》中明确要求各国通过国家机制实施各自的空间碎片减缓措施。作为新兴空间大国,印度比较重视空间碎片的减缓问题。印度通过其国家机制,具体而言是通过许多机构和部门的协调工作,尽可能地执行减缓措施,规范国内的空间活动。印度建立国家机制的经验对中国的相关工作具有重要借鉴意义。     

Performance of the shrinkage preliminary test ridge regression estimators based on the conflicting of W,LR and LM tests

The shrinkage preliminary test ridge regression estimators (SPTRRE) based on the Wald (W), the likelihood ratio (LR) and the Lagrangian multiplier (LM) tests are considered in this paper. The bias and the risk functions of the proposed estimators are derived. The regions of optimality of the estimators are determined under the quadratic risk function. Under the null hypothesis, the SPTRRE based on LM test has the smallest risk, followed by the estimators based on LR and W tests. However, the SPTRRE based on W test performs the best followed by the LR and LM based estimators when the parameter moves away from the subspace of the restrictions. The conditions of superiority of the proposed estimator for both ridge and departure parameters are discussed. The optimum choice of the level of significance becomes the traditional choice by using the W test for all non-negative ridge parameters.     

Goodness‐of‐fit testing based on a weighted bootstrap: A fast large‐sample alternative to the parametric bootstrap

The process comparing the empirical cumulative distribution function of the sample with a parametric estimate of the cumulative distribution function is known as the empirical process with estimated parameters and has been extensively employed in the literature for goodness‐of‐fit testing. The simplest way to carry out such goodness‐of‐fit tests, especially in a multivariate setting, is to use a parametric bootstrap. Although very easy to implement, the parametric bootstrap can become very computationally expensive as the sample size, the number of parameters, or the dimension of the data increase. An alternative resampling technique based on a fast weighted bootstrap is proposed in this paper, and is studied both theoretically and empirically. The outcome of this work is a generic and computationally efficient multiplier goodness‐of‐fit procedure that can be used as a large‐sample alternative to the parametric bootstrap. In order to approximately determine how large the sample size needs to be for the parametric and weighted bootstraps to have roughly equivalent powers, extensive Monte Carlo experiments are carried out in dimension one, two and three, and for models containing up to nine parameters. The computational gains resulting from the use of the proposed multiplier goodness‐of‐fit procedure are illustrated on trivariate financial data. A by‐product of this work is a fast large‐sample goodness‐of‐fit procedure for the bivariate and trivariate t distribution whose degrees of freedom are fixed. The Canadian Journal of Statistics 40: 480–500; 2012 © 2012 Statistical Society of Canada     

A powerful and interpretable alternative to the Jarque–Bera test of normality based on 2nd-power skewness and kurtosis,using the Rao's score test on the APD family

We introduce the 2nd-power skewness and kurtosis, which are interesting alternatives to the classical Pearson's skewness and kurtosis, called 3rd-power skewness and 4th-power kurtosis in our terminology. We use the sample 2nd-power skewness and kurtosis to build a powerful test of normality. This test can also be derived as Rao's score test on the asymmetric power distribution, which combines the large range of exponential tail behavior provided by the exponential power distribution family with various levels of asymmetry. We find that our test statistic is asymptotically chi-squared distributed. We also propose a modified test statistic, for which we show numerically that the distribution can be approximated for finite sample sizes with very high precision by a chi-square. Similarly, we propose a directional test based on sample 2nd-power kurtosis only, for the situations where the true distribution is known to be symmetric. Our tests are very similar in spirit to the famous Jarque–Bera test, and as such are also locally optimal. They offer the same nice interpretation, with in addition the gold standard power of the regression and correlation tests. An extensive empirical power analysis is performed, which shows that our tests are among the most powerful normality tests. Our test is implemented in an R package called PoweR.     

A Robust Approach for Mitigating Risks in Cyber Supply Chains

In recent years, there have been growing concerns regarding risks in federal information technology (IT) supply chains in the United States that protect cyber infrastructure. A critical need faced by decisionmakers is to prioritize investment in security mitigations to maximally reduce risks in IT supply chains. We extend existing stochastic expected budgeted maximum multiple coverage models that identify “good” solutions on average that may be unacceptable in certain circumstances. We propose three alternative models that consider different robustness methods that hedge against worst‐case risks, including models that maximize the worst‐case coverage, minimize the worst‐case regret, and maximize the average coverage in the $(1?\alpha )$ worst cases (conditional value at risk). We illustrate the solutions to the robust methods with a case study and discuss the insights their solutions provide into mitigation selection compared to an expected‐value maximizer. Our study provides valuable tools and insights for decisionmakers with different risk attitudes to manage cybersecurity risks under uncertainty.     

Public Attitudes Toward Siting a High-Level Nuclear Waste Repository in Nevada

This paper examines the sources of public opposition to a high-level nuclear waste repository among samples of 1001 residents of Nevada and a national sample of 1201 residents. Two models of choice are contrasted: A benefit-cost model and a risk-perception model of individual choice. The data suggest that the willingness of Nevada residents to accept a repository at Yucca Mountain depends upon subjective risk factors, especially the perceived seriousness of risk to future generations. Perceived risk depends in part on level of trust placed in the Department of Energy to manage a repository safely. Opposition to a local repository did not decrease significantly if compensation in the form of annual rebates, either ($1000, $3000, or $5000 per year for 20 years) were offered to residents. The public needs to be convinced before compensation is considered, that the repository will possess minimal risks to themselves as well as to future generations, and that the site currently targeted is suitable. One way to do this is through adoption of mitigation and control procedures such as strict federal standards and local control over the operation of the repository. The federal government should also consider returning to the fair procedure for selection between candidate sites specified in the initial Nuclear Waste Policy Act of 1982.     

拥堵交通网络模型和增强拉格朗日乘子算法

为了更加准确地反映拥堵网络的交通流状态,必须在传统交通网络均衡模型中添加路段容量约束条件,限制路段交通流量的非现实的增长,因此构造了一个容量制约下的均衡交通网络流模型.在拥堵交通网络中,传统的路段特征函数不能反映拥堵的交通特性,修正路段的旅行费用表示为行车时间和因为拥堵而产生的等待延迟的总和,路段容量约束条件的拉格朗日乘子等于该路段的等待延迟.把外惩罚函数和牛顿法相结合构筑成增强拉格朗日乘子算法,用来求解拥堵网络的交通流状态.外惩罚函数通过调整惩罚参数,把容量约束下的网络均衡问题转化成传统网络均衡问题.牛顿法通过移动方向、修正矩阵和移动步长的组合来保证路径或路段交通流量解的可行性,同时获得转化后子问题的最优解.     

天基碎片主动移除的非军事化界限

国际空间法体系主要规范空间碎片减缓措施,对能够切实减少碎片数量的碎片主动移除措施尚未形成统一规定。由于空间碎片的法律概念和所有权、管辖权的规定尚存立法空白,天基空间碎片主动移除可能被视为空间军事活动,进而引发外空军事化的争议以及违反和平利用外层空间原则及禁止使用武力原则的合法性争议。厘清天基碎片主动移除的非军事化界限有助于国际社会统一主动移除的法律定性。对现行的国际空间条约的分析表明,空间碎片具有"潜在有害干扰"的法律特征,会对正常空间活动造成负面影响。因此主动移除碎片应遵循"消除空间碎片对空间活动的危害"的基本准则,据此进行的碎片移除不应属于外空军事化或非法使用武力的范畴。     

Rating Threat Mitigators: Faith in Experts,Governments, and Individuals Themselves to Create a Safer World

This research explores public judgments about the threat-reducing potential of experts, individual behavior, and government spending. The data are responses of a national sample of 1225 to mail surveys that include measures of several dimensions of public judgments about violent crime, automobile accidents, hazardous chemical waste, air pollution, water pollution, global warming, AIDS, heart disease, and cancer. Beliefs about who can best mitigate threats are specific to classes of threats. In general, there is little faith that experts can do much about violent crime and automobile accidents, moderate faith in their ability to address problems of global warming, and greater expectations for expert solutions to the remaining threats. People judge individual behavior as effective in reducing the threats of violent crime, AIDS, heart disease, and automobile accidents but less so for the remaining threats. Faith in more government spending is highest for AIDS and the other two health items, lowest for the trio of violent crime, automobile accidents, and global warming, and moderate for the remaining threats. For most threats, people are not distributed at the extremes in judging mitigators. Strong attitudinal and demographic cleavages are also lacking, although some interesting relationships occur. This relative lack of sharp cleavages and the generally moderate opinion indicate ample opportunity for public education and risk communication.