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.     

Predicting the Risk of Biological Invasions Using Environmental Similarity and Transport Network Connectedness

Understanding the risk of biological invasions associated with particular transport pathways and source regions is critical for implementing effective biosecurity management. This may require both a model for physical connectedness between regions, and a measure of environmental similarity, so as to quantify the potential for a species to be transported from a given region and to survive at a destination region. We present an analysis of integrated biosecurity risk into Australia, based on flights and shipping data from each global geopolitical region, and an adaptation of the “range bagging” method to determine environmental matching between regions. Here, we describe global patterns of environmental matching and highlight those regions with many physical connections. We classify patterns of global invasion risk (high to low) into Australian states and territories. We validate our analysis by comparison with global presence data for 844 phytophagous insect pest species, and produce a list of high‐risk species not previously known to be present in Australia. We determined that, of the insect pest species used for validation, the species most likely to be present in Australia were those also present in geopolitical regions with high transport connectivity to Australia, and those regions that were geographically close, and had similar environments.     

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.     

CAN THE PRESIDENT REALLY AFFECT ECONOMIC GROWTH? PRESIDENTIAL EFFORT AND THE POLITICAL BUSINESS CYCLE

Presidential elections are often seen as referendums on the health of the economy; however, little evidence exists on the president's ability to influence gross domestic product (GDP). This study examines the effect of the incentive to be reelected and the resulting increase in presidential effort on GDP growth. Growth is found to rise in reelection years for first‐term presidents after 1932 and to fall in election years before 1932, when reelection was uncommon, and for second‐term presidents generally. This effect is largest for high‐quality presidents—who probably have the highest return to effort—and is spread across multiple sectors of the economy. (JEL D78, D72, E32, J24)