Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery

Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.     

Reducing sample size needed for cox-proportional hazards model analysis using more efficient sampling method

Abstract

In general, survival data are time-to-event data, such as time to death, time to appearance of a tumor, or time to recurrence of a disease. Models for survival data have frequently been based on the proportional hazards model, proposed by Cox. The Cox model has intensive application in the field of social, medical, behavioral and public health sciences. In this paper we propose a more efficient sampling method of recruiting subjects for survival analysis. We propose using a Moving Extreme Ranked Set Sampling (MERSS) scheme with ranking based on an easy-to-evaluate baseline auxiliary variable known to be associated with survival time. This paper demonstrates that this approach provides a more powerful testing procedure as well as a more efficient estimate of hazard ratio than that based on simple random sampling (SRS). Theoretical derivation and simulation studies are provided. The Iowa 65+ Rural study data are used to illustrate the methods developed in this paper.     

On Optimality of Bayesian Wavelet Estimators

Abstract.  We investigate the asymptotic optimality of several Bayesian wavelet estimators, namely, posterior mean, posterior median and Bayes Factor, where the prior imposed on wavelet coefficients is a mixture of a mass function at zero and a Gaussian density. We show that in terms of the mean squared error, for the properly chosen hyperparameters of the prior, all the three resulting Bayesian wavelet estimators achieve optimal minimax rates within any prescribed Besov space     for p  ≥ 2. For 1 ≤  p  < 2, the Bayes Factor is still optimal for (2 s +2)/(2 s +1) ≤  p  < 2 and always outperforms the posterior mean and the posterior median that can achieve only the best possible rates for linear estimators in this case.     

Computer support of placement planning: the use of expert systems in child welfare

Computers have become commonplace in nearly every setting, from home to office. In addition to their usefulness for managerial tasks, they have the potential to aid the primary decision-making tasks of professional social service workers. The benefits and background of such "expert systems" are explored, and the structure of one experimental consulting program for child welfare workers, PLACECON, is presented.     

Have Labor Market Reforms at the Turn of the Millennium Changed the Job and Employment Durations of new Entrants?

Many European countries during the 1990s and early 2000s tried to raise labor market flexibility by relaxing their employment protection legislation. This paper investigates whether the reforms have influenced job and employment stability for labor market entrants in Germany and Italy. Duration models are estimated using comparable administrative data for the two countries. The results provide, to some extent, evidence of a decrease in job stability. However, this is not accompanied by a compensating increase in employment stability during the first 3 years after labor market entry.     

Mental Illness and Criminal Behavior

The tragic events in Aurora, CO and Newtown, CT have renewed public perception of mentally persons as ‘dangerous’ and ‘criminal.’ Unfortunately, this perception is based more on conjecture and fear than research. The following essay takes stock of the empirical research on mental illness and criminal behavior. Three noteworthy trends emerge from this literature. First, the prevalence of mental illness is substantially higher among individuals who have come in contact with the criminal justice system relative to the general population. Second, individuals with psychotic and externalizing behavioral disorders, particularly those who also abuse drugs and alcohol, tend to engage in higher levels of violence than individuals with other forms of mental illness. Third, mental illness does not determine whether someone will break the law; rather, it is but one of many criminogenic risk factors that interact in complex ways to influence individual behavior.     

Reputation spillover effects from grant‐providing institutions

There is a broad academic discussion about the impact of funding grants from a foundation or a government department on individual support intentions toward the nonprofit organization receiving the grant. However, the role of the grant provider's reputation has frequently been overlooked. In this study, we experimentally tested whether there is a reputation spillover effect of a grant‐providing organization. Based on a real‐life example, we asked citizens to rate their willingness to donate to a nonprofit organization, and we experimentally manipulated the available information on funding sources. We test this for both a government department and a foundation as a grant provider. Our results suggest that not the act of receiving a grant, but the citizens' awareness about the funding organization—at least in the case of a foundation—has an impact on support intentions. In contrast, for a prominent government department as a grant provider, we did not find support for a reputation spillover effect.