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.     

Cross‐Sectional Psychological and Demographic Associations of Zika Knowledge and Conspiracy Beliefs Before and After Local Zika Transmission

Perceptions of infectious diseases are important predictors of whether people engage in disease‐specific preventive behaviors. Having accurate beliefs about a given infectious disease has been found to be a necessary condition for engaging in appropriate preventive behaviors during an infectious disease outbreak, while endorsing conspiracy beliefs can inhibit preventive behaviors. Despite their seemingly opposing natures, knowledge and conspiracy beliefs may share some of the same psychological motivations, including a relationship with perceived risk and self‐efficacy (i.e., control). The 2015–2016 Zika epidemic provided an opportunity to explore this. The current research provides some exploratory tests of this topic derived from two studies with similar measures, but different primary outcomes: one study that included knowledge of Zika as a key outcome and one that included conspiracy beliefs about Zika as a key outcome. Both studies involved cross‐sectional data collections that occurred during the same two periods of the Zika outbreak: one data collection prior to the first cases of local Zika transmission in the United States (March–May 2016) and one just after the first cases of local transmission (July–August). Using ordinal logistic and linear regression analyses of data from two time points in both studies, the authors show an increase in relationship strength between greater perceived risk and self‐efficacy with both increased knowledge and increased conspiracy beliefs after local Zika transmission in the United States. Although these results highlight that similar psychological motivations may lead to Zika knowledge and conspiracy beliefs, there was a divergence in demographic association.     

Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.     

Feminized Forestry: The Promises and Pitfalls of Change in a Masculine Organization

The gendering of forestry as a distinctly masculine profession has led to a wide range of negative outcomes, including legal actions concerning discrimination, poor public perceptions and poor environmental records. Forestry organizations have addressed these concerns by attempting to increase the number of women in the profession. These efforts have been largely ineffective. Using the case of community‐based forestry, I argue that when we begin to consider not only women but also normatively feminine values as agents of change, our understanding of the profession of forestry may be rejuvenated.     

Properties of the weighted log‐rank test in the design of confirmatory studies with delayed effects

Proportional hazards are a common assumption when designing confirmatory clinical trials in oncology. This assumption not only affects the analysis part but also the sample size calculation. The presence of delayed effects causes a change in the hazard ratio while the trial is ongoing since at the beginning we do not observe any difference between treatment arms, and after some unknown time point, the differences between treatment arms will start to appear. Hence, the proportional hazards assumption no longer holds, and both sample size calculation and analysis methods to be used should be reconsidered. The weighted log‐rank test allows a weighting for early, middle, and late differences through the Fleming and Harrington class of weights and is proven to be more efficient when the proportional hazards assumption does not hold. The Fleming and Harrington class of weights, along with the estimated delay, can be incorporated into the sample size calculation in order to maintain the desired power once the treatment arm differences start to appear. In this article, we explore the impact of delayed effects in group sequential and adaptive group sequential designs and make an empirical evaluation in terms of power and type‐I error rate of the of the weighted log‐rank test in a simulated scenario with fixed values of the Fleming and Harrington class of weights. We also give some practical recommendations regarding which methodology should be used in the presence of delayed effects depending on certain characteristics of the trial.     

Talking Prose All These Years: Agent‐Based Modeling as Process‐Oriented Analysis

This article considers the implications of an approach to computer simulation called agent‐based modeling for process‐oriented analysis. It argues that many theoretical and methodological debates found in the latter field can be effectively advanced by the former. The argument is presented and then extended using a ubiquitous agent‐based model proposed to improve understanding of ethnic residential segregation. The argument has three strands. The first is that theoretical and methodological debates are unlikely to progress unless they can be “cashed out” empirically. The second is that agent‐based modeling (and its distinctive methodology) has capabilities to do this that existing research methods lack and, in fact, that agent‐based models are a natural way to represent “social process” as apparently conceived by process‐oriented analysis. The third is that possibilities exist for productive synthesis between agent‐based modeling and process‐oriented analysis with the former clarifying, instantiating, and perhaps even testing notions of process developed by the latter.     

Cyclical Evolution of the Global Right

An understanding of the current right‐wing national and transnational social movements can benefit from comparing them to the global and national conditions operating during their last appearance in the first half of the twentieth century and by carefully comparing twentieth‐century fascism with the neofascist and right‐wing populist movements that have been emerging in the twenty‐first century. This allows us to assess the similarities and differences, and to gain insights about what could be the consequences of the reemergence of populist nationalism and fascist movements. Our study uses the comparative evolutionary world‐systems perspective to study the Global Right from 1800 to the present. We see fascism as a form of capitalism that emerges when the capitalist project is in crisis. World historical waves of right‐wing populism and fascism are caused by the cycles of globalization and deglobalization, the rise and fall of hegemonic core powers, long business cycles (the Kondratieff wave), and interactions with both Centrist Liberalism and the Global Left. We consider how crises of the global capitalist system have produced right‐wing backlashes in the past, and how a future terminal crisis of capitalism could lead to a reemergence of a new form of authoritarian global governance or a reorganized global democracy in the future.