Understanding Community Resilience from a PRA Perspective Using Binary Decision Diagrams

Probabilistic risk assessment (PRA) is a useful tool to assess complex interconnected systems. This article leverages the capabilities of PRA tools developed for industrial and nuclear risk analysis in community resilience evaluations by modeling the food security of a community in terms of its built environment as an integrated system. To this end, we model the performance of Gilroy, CA, a moderate‐size town, with regard to disruptions in its food supply caused by a severe earthquake. The food retailers of Gilroy, along with the electrical power network, water network elements, and bridges are considered as components of a system. Fault and event trees are constructed to model the requirements for continuous food supply to community residents and are analyzed efficiently using binary decision diagrams (BDDs). The study also identifies shortcomings in approximate classical system analysis methods in assessing community resilience. Importance factors are utilized to rank the importance of various factors to the overall risk of food insecurity. Finally, the study considers the impact of various sources of uncertainties in the hazard modeling and performance of infrastructure on food security measures. The methodology can be applicable for any existing critical infrastructure system and has potential extensions to other hazards.     

Production systems and supplier selection: a multi-phase process model

Abstract

Purpose: This paper presents a combined multi-phase supplier selection model. The process repeatedly revisits the criteria and sourcing decision as the development process continues. This enables a structured adoption of product and production system innovation from strategic suppliers, where previously the literature purely focuses on product innovation or cost reduction. Design/methodology/approach: The authors adopted an embedded researcher style, inductive, qualitative case study of an industrial supply cluster comprising a focal automotive company and its interaction with three different strategic stamping suppliers. Findings: Our contribution is the multi-phased production and product innovation process. This is an advance from traditional supplier selection and also an extension of ideas of supplier-located product development as it includes production system development, and complements the literature on working with strategic suppliers. Specifically, we explicitly articulate the previously unreported issue of whether a supplier chosen for its innovation capabilities at the start of the new product development process will also be the most appropriate supplier during the production system development phase, when an ability to work collaboratively may be the most important attribute, or in the large-scale production phase when an ability to manufacture at low unit cost may be most important. Originality/value: The paper identifies a multi-phase approach to tendering within a fixed body of strategic suppliers which seeks to identify the optimum technological and process decisions as well as the traditional supplier sourcing choice. These areas have not been combined before and generate a valuable approach for firms to adopt as well as for researchers to extend our understanding of a highly complex process.     

Hypothesis Tests for Neyman's Bias in Case–Control Studies

Survival bias is a long recognized problem in case–control studies, and many varieties of bias can come under this umbrella term. We focus on one of them, termed Neyman's bias or ‘prevalence–incidence bias’. It occurs in case–control studies when exposure affects both disease and disease-induced mortality, and we give a formula for the observed, biased odds ratio under such conditions. We compare our result with previous investigations into this phenomenon and consider models under which this bias may or may not be important. Finally, we propose three hypothesis tests to identify when Neyman's bias may be present in case–control studies. We apply these tests to three data sets, one of stroke mortality, another of brain tumors, and the last of atrial fibrillation, and find some evidence of Neyman's bias in the former two cases, but not the last case.     

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.     

Early socialization of hostile attribution bias: The roles of parental attributions,parental discipline,and child attributes

The goal of this study was to examine child and parent predictors of children's hostile attribution bias (HAB) with a particular focus on exploring the associations between parents’ early attribution of child misbehavior and children's HAB in the transition to school age. Participants were 241 children (118 girls) of middle‐income families who were at risk for school‐age conduct problems. Multi‐method, multi‐informant data were collected on maternal attributions of child misbehavior, parental use of corporal punishment, and child attributes (i.e., verbal IQ, effortful control, theory of mind, and emotional understanding) at 3 years, and child HAB in ambiguous situations at 6 years. Results indicated that mothers’ internal explanations for children's misconduct may either reduce or increase children's later HAB depending on the specific content of attributions, such that mothers’ belief that children misbehave because of their internal state (i.e., emotional state or temperament) was associated with lower levels of child HAB, whereas attributing power‐based motives (i.e., manipulative, controlling intentions) in children was associated with higher levels of HAB. The findings are discussed with respect to appreciating the complexity of parents’ explanations for children's behavior, and considering parental cognition as a potential target for early identification and prevention of child HAB and related problems.     

Estimation of Mode Using Two-phase Sampling

In this article, we consider the problem of estimation of mode using two-phase sampling. Ratio- and difference-type estimators in two-phase sampling are proposed. The asymptotic properties of the proposed estimators are studied analytically as well as empirically for different situations for the given cost of surveys. The use of two-phase sampling has been found to be cost saving design while estimating mode by making a proper use of auxiliary information.     

Can Carbon Nanomaterials Improve CZTS Photovoltaic Devices? Evaluation of Performance and Impacts Using Integrated Life‐Cycle Assessment and Decision Analysis

In emergent photovoltaics, nanoscale materials hold promise for optimizing device characteristics; however, the related impacts remain uncertain, resulting in challenges to decisions on strategic investment in technology innovation. We integrate multi‐criteria decision analysis (MCDA) and life‐cycle assessment (LCA) results (LCA‐MCDA) as a method of incorporating values of a hypothetical federal acquisition manager into the assessment of risks and benefits of emerging photovoltaic materials. Specifically, we compare adoption of copper zinc tin sulfide (CZTS) devices with molybdenum back contacts to alternative devices employing graphite or graphene instead of molybdenum. LCA impact results are interpreted alongside benefits of substitution including cost reductions and performance improvements through application of multi‐attribute utility theory. To assess the role of uncertainty we apply Monte Carlo simulation and sensitivity analysis. We find that graphene or graphite back contacts outperform molybdenum under most scenarios and assumptions. The use of decision analysis clarifies potential advantages of adopting graphite as a back contact while emphasizing the importance of mitigating conventional impacts of graphene production processes if graphene is used in emerging CZTS devices. Our research further demonstrates that a combination of LCA and MCDA increases the usability of LCA in assessing product sustainability. In particular, this approach identifies the most influential assumptions and data gaps in the analysis and the areas in which either engineering controls or further data collection may be necessary.     

The effect of cognitive diversity on the illusion of control bias in strategic decisions: An experimental investigation

By integrating cognitive diversity into debiasing literature, this paper contributes towards opening the black box of executive judgment. Based on information processing theory we investigate the role of cognitive diversity in strategic decision making. We apply a vignette-based experimental research design to examine the effect of cognitive diversity in teams on decision maker's illusion of control. The results of these experiments provide evidence for a positive influence of high cognitive diversity for debiasing judgment while similarly indicating no such effect for groups with low cognitive diversity. These findings suggest that group composition aspects can play an important role for improving judgment in decision making teams and open promising new avenues for studying debiasing in behavioral strategy research.     

Countertransference Bias: Self-Examination,Not Cross-Examination

ABSTRACT

In reply to T. Tippins (2007, this issue), we take issue with his claim that recognition of the biasing impact of custody evaluators' countertransference opens the door to “evidentially legitimate” fishing expeditions through evaluators' private lives on cross-examination. We clarify the distinction between self-examination for counter-transference bias as a heuristic for improving the sensitivity and objectivity of expert testimony and its use as substantive evidence. We argue that countertransference bias is a highly personal, emotionally charged kind of bias, unlike the cognitive biases more frequently discussed in the child custody literature. We conclude that recognition of the insidious emotional influences that threaten our objectivity in working with families in high conflict divorce is an important ethical responsibility.     

The Inability of Self-affirmations to Decrease Defensive Bias Toward an Alcohol-Related Risk Message Among High-Risk College Students

Abstract

Objective: To examine the efficacy of a self-affirmation task in deterring college alcohol misuse and the importance of preexisting beliefs in predicting subsequent behavior change. Participants: Heavy-drinking undergraduates (N = 110) participated during the 2011–2012 academic year. Methods: Participants were randomized to complete an affirmation or control task before reading an alcohol risk message. Alcohol-related beliefs and behaviors were assessed. Participants completed a 2-week online follow-up assessing alcohol-related behaviors. Results: Both groups reported increased perceived problem importance, but neither group displayed changes in personal risk. Follow-up assessment revealed similar, significant declines in peak consumption in both groups, with no significant between-group differences. Preexisting beliefs accounted for 5% to 10% of variance in drinking outcomes. Conclusions: An affirmation task does not seem to decrease defensive processing or alter high-risk drinking behaviors among college students and should not be utilized in lieu of more effective strategies.