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.     

互联网深化、信息不确定性与企业出口平稳性

互联网作为信息搜寻和传播的重要渠道,有利于缓解国际市场不确定性对企业出口决策的影响,从而有助于稳定企业出口预期,保障出口企业的持续生存。本文首先基于Fernandes 和 Tang(2014)的研究框架将互联网深化程度和出口集聚同时纳入到异质性贸易企业模型,在剥离出口集聚对企业出口行为影响的基础上,从理论视角揭示了互联网深化程度提高影响企业出口行为特别是出口持续生存的内在机理与方向。研究发现,不同于出口集聚会同时影响企业的需求冲击和成本冲击,互联网深化带来的信息溢出效应会通过贝叶斯法则(Bayesian Rule)使得企业修正其对国际市场不确定性的预期,提高其对目的国市场成本冲击的估计精度,从而在互联网深化程度较高的城市,企业进入出口市场的临界生产率较低,有利于其出口参与度和出口稳定性的提高。然而互联网深化程度提高不影响企业在目的国市场产品出口的集约边际。本文使用2000-2013 年中国工业企业数据库、中国海关数据库和中国城市统计年鉴数据库的匹配数据从企业-目的国-产品层面实证检验了本文的理论命题。研究表明,我国政府应充分发挥互联网发展降低信息不确定性的微观机制,以推动外贸的平稳增长。     

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.     

审计定价管制与交易剩余——2010年审计定价管制失效的自然实验

在经济下行压力加大、资本市场进一步开放的新形势下,厘清审计市场交易——监管机制,完善审计服务市场尤为必要。借由2010 年审计定价管制政策失效的自然实验,本文通过嵌入双边随机边界模型,得到审计双方的定价交易剩余指标,运用双重差分模型解析价格管制与交易定价的作用机制。研究发现,定价管制失效的原因不在于规制俘获,而在于价格管制与当前的市场效率不匹配。下限管制尽管能够提高审计师剩余,但同时会放大交易定价风险,增加剩余的错配,扰乱交易秩序。上限管制则进一步固化市场的低价竞争。进一步研究发现审计师剩余与盈余质量显著相关,2014年的放开定价管制政策提高了审计师剩余。研究厘清了审计市场交易机制,有利于未来研究审计交易机制的微观影响及与盈余质量的关联,为在新时代把握审计市场交易——监管规律、培育自发良性交易的审计市场提供有益借鉴。