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.     

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.”     

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.     

城市经济圈发展助推区域一体化——兼论南沙新区与港澳的经济合作

根据区域一体化理论,结合新时期广东省在推进珠三角一体化中,建立和发展广佛肇、深莞惠和珠中江三个经济圈的实践,分析了区域经济一体化对区域整体发展的新意义、地位和作用,讨论了珠江三角洲通过建设和发展若干个以经济发展为核心的现代都市圈,对于新时期加强与港澳经济合作,促进区域经济一体化的更好发展颇具深远的意义和作用。     

Community Resilience and Decision Theory Challenges for Catastrophic Events

Extreme and catastrophic events pose challenges for normative models of risk management decision making. They invite development of new methods and principles to complement existing normative decision and risk analysis. Because such events are rare, it is difficult to learn about them from experience. They can prompt both too little concern before the fact, and too much after. Emotionally charged and vivid outcomes promote probability neglect and distort risk perceptions. Aversion to acting on uncertain probabilities saps precautionary action; moral hazard distorts incentives to take care; imperfect learning and social adaptation (e.g., herd‐following, group‐think) complicate forecasting and coordination of individual behaviors and undermine prediction, preparation, and insurance of catastrophic events. Such difficulties raise substantial challenges for normative decision theories prescribing how catastrophe risks should be managed. This article summarizes challenges for catastrophic hazards with uncertain or unpredictable frequencies and severities, hard‐to‐envision and incompletely described decision alternatives and consequences, and individual responses that influence each other. Conceptual models and examples clarify where and why new methods are needed to complement traditional normative decision theories for individuals and groups. For example, prospective and retrospective preferences for risk management alternatives may conflict; procedures for combining individual beliefs or preferences can produce collective decisions that no one favors; and individual choices or behaviors in preparing for possible disasters may have no equilibrium. Recent ideas for building “disaster‐resilient” communities can complement traditional normative decision theories, helping to meet the practical need for better ways to manage risks of extreme and catastrophic events.     

“Discrimination” versus “Unfair Treatment”: Measuring Differential Treatment and its Association with Health

There is consistent evidence of the health‐harming effects of discrimination. However, it remains unclear whether discrimination contributes to persistent racial and ethnic health disparities. One hindrance to documenting the association between discrimination and health disparities is ongoing methodological issues, particularly the role of question wording in assessing self‐reports of discrimination. Using two nationally representative surveys, we investigate whether the prevalence, distribution, and mental and physical health consequences of differential treatment vary by question wording—”discrimination” versus “unfair treatment.” We find that “unfair treatment” yields greater reports of everyday forms of differential treatment relative to reports of “discrimination,” while the latter yields greater reports of major forms of differential treatment. In addition, the negative effect of “unfair treatment” on mental health is stronger than that of “discrimination,” while the latter has a stronger negative effect on physical health. However, the effect of question wording on reports of differential treatment and its association with health is largely unique to non‐Hispanic whites. We conclude that unfair treatment and discrimination reflect distinct concepts that should not be used interchangeably.     

Preference for realistic art predicts support for Brexit

Following the UK’s EU referendum in June 2016, there has been considerable interest from scholars in understanding the characteristics that differentiate Leave supporters from Remain supporters. Since Leave supporters score higher on conscientiousness but lower on neuroticism and openness, and given their general proclivity toward conservatism, we hypothesized that preference for realistic art would predict support for Brexit. Data on a large nationally representative sample of the British population were obtained, and preference for realistic art was measured using a four‐item binary choice test. Controlling for a range of personal characteristics, we found that respondents who preferred all four realistic paintings were 15–20 percentage points more likely to support Leave than those who preferred zero or one realistic paintings. This effect was comparable to the difference in support between those with a degree and those with no education, and was robust to controlling for the respondent’s party identity.