Improving Risk Management: From Lame Excuses to Principled Practice

The three classic pillars of risk analysis are risk assessment (how big is the risk and how sure can we be?), risk management (what shall we do about it?), and risk communication (what shall we say about it, to whom, when, and how?). We propose two complements as important parts of these three bases: risk attribution (who or what addressable conditions actually caused an accident or loss?) and learning from experience about risk reduction (what works, and how well?). Failures in complex systems usually evoke blame, often with insufficient attention to root causes of failure, including some aspects of the situation, design decisions, or social norms and culture. Focusing on blame, however, can inhibit effective learning, instead eliciting excuses to deflect attention and perceived culpability. Productive understanding of what went wrong, and how to do better, thus requires moving past recrimination and excuses. This article identifies common blame‐shifting “lame excuses” for poor risk management. These generally contribute little to effective improvements and may leave real risks and preventable causes unaddressed. We propose principles from risk and decision sciences and organizational design to improve results. These start with organizational leadership. More specifically, they include: deliberate testing and learning—especially from near‐misses and accident precursors; careful causal analysis of accidents; risk quantification; candid expression of uncertainties about costs and benefits of risk‐reduction options; optimization of tradeoffs between gathering additional information and immediate action; promotion of safety culture; and mindful allocation of people, responsibilities, and resources to reduce risks. We propose that these principles provide sound foundations for improving successful risk management.     

Practical procedures to deal with common support problems in matching estimation

This paper assesses the performance of common estimators adjusting for differences in covariates, such as matching and regression, when faced with the so-called common support problems. It also shows how different procedures suggested in the literature affect the properties of such estimators. Based on an empirical Monte Carlo simulation design, a lack of common support is found to increase the root-mean-squared error of all investigated parametric and semiparametric estimators. Dropping observations that are off support usually improves their performance, although the magnitude of the improvement depends on the particular method used.     

Schools,Bullying, and Inequality: Intersecting Factors and Complexities with the Stratification of Youth Victimization at School

Bullying within United States (US) schools is a growing concern among parents, school officials and policymakers. In early 2011, the first‐ever White House Conference on Bullying Prevention was held in hope of addressing bullying within US schools. Although the social, political, and media attention is increasing, it is important to consider the complexities and disparities associated with school bullying. In this article, four of the wide array of influences that increase the vulnerabilities of youth to be a victim of bullying at school are reviewed: (i) race and ethnicity, (ii) being and immigrant, (iii) gender, and; (iv) sexual orientation. Understanding and acknowledging the inequalities associated with school bullying, as well as the policies implemented in response, is instrumental for the US’ efforts towards providing safe, healthy, and democratic learning environments.     

Sexual Harassment,Psychological Distress,and Problematic Drinking Behavior Among College Students: An Examination of Reciprocal Causal Relations

Sexual harassment on college campuses is a frequent occurrence and serious public health concern. Victims of sexual harassment are at risk for many possible negative health consequences. In addition, certain psychological distress symptoms and/or alcohol use may put individuals at increased risk of being victims of sexual harassment. Data from more than 2,000 college students in the Midwestern United States were used to examine reciprocal causal effects of the relations between (a) experiencing sexual harassment and alcohol use and (b) experiencing sexual harassment and psychological distress symptoms, specifically depression and anger/hostility. Analyses were conducted separately for sexual harassment which occurs at school and which occurs in college students’ workplaces, and also separately for men and women. Results of cross-lagged panel models showed that there were reciprocal causal effects between sexual harassment and alcohol problems, depression, and anger. Discussion focuses on the overall patterns of results as well as the nuances within these findings.     

Why Frequency is Not Well Defined for Engineering Systems with Nonexponential Failure Times

Dr. Yellman proposes to define frequency as “a time‐rate of events of a specified type over a particular time interval.” We review why no definition of frequency, including this one, can satisfy both of two conditions: (1) the definition should agree with the ordinary meaning of frequency, such as that less frequent events are less likely to occur than more frequent events, over any particular time interval for which the frequencies of both are defined; and (2) the definition should be applicable not only to exponentially distributed times between (or until) events, but also to some nonexponential (e.g., uniformly distributed) times. We make the simple point that no definition can satisfy (1) and (2) by showing that any definition that determines which of any two uniformly distributed times has the higher “frequency” (or that determines that they have the same “frequency,” if neither is higher) must assign a higher frequency number to the distribution with the lower probability of occurrence over some time intervals. Dr. Yellman's proposed phrase, “time‐rate of events … over a particular time interval” is profoundly ambiguous in such cases, as the instantaneous failure rates vary over an infinitely wide range (e.g., from one to infinity), making it unclear which value is denoted by the phrase “time‐rate of events.”     

The Impact of Social Connectedness and Internalized Transphobic Stigma on Self-Esteem Among Transgender and Gender Non-Conforming Adults

The transgender and gender non-conforming (TGNC) community continues to represent a notably marginalized population exposed to pervasive discrimination, microaggressions, and victimization. Congruent with the minority stress model, TGNC individuals persistently experience barriers to wellbeing in contemporary society; however, research uncovering resilience-based pathways to health among this population is sparse. This study aimed to explore the impact and interaction between internalized transphobic stigma and a potential buffer against minority stress—social connectedness—on the self-esteem of TGNC identified adults. Data were collected from 65 TGNC identified adults during a national transgender conference. Multiple regression analysis reveals that self-esteem is negatively impacted by internalized transphobia and positively impacted by social connectedness. Social connectedness did not significantly moderate the relationship between internalized transphobia and self-esteem. Micro and macro interventions aimed at increasing social connectedness and decreasing internalized transphobic stigma may be paramount for enhancing resiliency and wellbeing in the TGNC community.     

Towards a transdisciplinary blueprint to studying bullying

Events related to bullying are so complex that they require a much broader examination than is usually available from a single disciplinary point of view. The purpose of the present paper is to offer new ways of examining this complex phenomenon. We will argue that transdisciplinarity (henceforth TD) could bring a more comprehensive analysis and a deeper understanding to the current state of bullying research. As a way of emphasizing the benefits of a more pluralistic and integrative approach, we will use TD based examples to describe the general context and process we used to develop a new definition of bullying, one that provides a sharper distinction between bullying and general aggression, thus offering the possibility of more targeted intervention strategies. The present paper is divided into three sections, namely, a selected review of the TD literature, an application of TD inspired ideas and tools that examine bullying from a number of interrelated perspectives, and an outline of a conceptual ‘Disciplinary Continuum’, as an example of how TD can be used to examine complex phenomena. Moreover, we will highlight TD’s expanding role in the funding process, as it is increasingly required by granting agencies and governmental ministries as a criterion for universities’ funding. We conclude by outlining ideas for further inquiries.     

Wiki-worthy: collective judgment of candidate notability

The use of socio-technical data to predict elections is a growing research area. We argue that election prediction research suffers from under-specified theoretical models that do not properly distinguish between ‘poll-like’ and ‘prediction market-like’ mechanisms understand findings. More specifically, we argue that, in systems with strong norms and reputational feedback mechanisms, individuals have market-like incentives to bias content creation toward candidates they expect will win. We provide evidence for the merits of this approach using the creation of Wikipedia pages for candidates in the 2010 US and UK national legislative elections. We find that Wikipedia editors are more likely to create Wikipedia pages for challengers who have a better chance of defeating their incumbent opponent and that the timing of these page creations coincides with periods when collective expectations for the candidate's success are relatively high.