Second-order refined peaks-over-threshold modelling for heavy-tailed distributions

Modelling excesses over a high threshold using the Pareto or generalized Pareto distribution (PD/GPD) is the most popular approach in extreme value statistics. This method typically requires high thresholds in order for the (G)PD to fit well and in such a case applies only to a small upper fraction of the data. The extension of the (G)PD proposed in this paper is able to describe the excess distribution for lower thresholds in case of heavy-tailed distributions. This yields a statistical model that can be fitted to a larger portion of the data. Moreover, estimates of tail parameters display stability for a larger range of thresholds. Our findings are supported by asymptotic results, simulations and a case study.     

Psycho-soziale Faktoren von Innovation

This paper investigates the relationship between social factors and innovation. A theoretical framework augmented by an appropriate terminology is suggested. The detailed review of current literature points out concrete relations between social factors and aspects of innovation. Finally, a critical discussion of ethical aspects of innovation is explicitly emphasized.     

Investment priorities and income distribution: The case of Brazil in 1970

In this paper, we study the conditions (ie., social disarticulation) by which choices in government policy priorities toward sectoral production may instigate increased income inequality. A dynamic multisectoral model is proposed in which the main link that is put forward is the necessary correspondence between rapid growth of production of certain types of goods and the expansion of demand for those same goods in the internal market. Application to Brazil illustrates the possibilities of such a tool for economic analysis and shows that the regressive wage policy implemented in Brazil was indeed consistent with that country's economic priorities and policy emphases.     

On “Black Swans” and “Perfect Storms”: Risk Analysis and Management When Statistics Are Not Enough

Two images, “black swans” and “perfect storms,” have struck the public's imagination and are used—at times indiscriminately—to describe the unthinkable or the extremely unlikely. These metaphors have been used as excuses to wait for an accident to happen before taking risk management measures, both in industry and government. These two images represent two distinct types of uncertainties (epistemic and aleatory). Existing statistics are often insufficient to support risk management because the sample may be too small and the system may have changed. Rationality as defined by the von Neumann axioms leads to a combination of both types of uncertainties into a single probability measure—Bayesian probability—and accounts only for risk aversion. Yet, the decisionmaker may also want to be ambiguity averse. This article presents an engineering risk analysis perspective on the problem, using all available information in support of proactive risk management decisions and considering both types of uncertainty. These measures involve monitoring of signals, precursors, and near‐misses, as well as reinforcement of the system and a thoughtful response strategy. It also involves careful examination of organizational factors such as the incentive system, which shape human performance and affect the risk of errors. In all cases, including rare events, risk quantification does not allow “prediction” of accidents and catastrophes. Instead, it is meant to support effective risk management rather than simply reacting to the latest events and headlines.     

Towards a Topology of ‘Doing Gender’: An Analysis of Empirical Research and Its Challenges

‘Doing gender’ is a much used term in research on gender, work and organizations. However, translating theoretical insight into empirical research is often a challenging endeavour. A lack of clarity with regard to the conceptualization and operationalization of key terms in turn often limits the theoretical and empirical purchase of a concept. The aim of this article is therefore to provide a systematization of empirical approaches to ‘doing gender’. This systematization leads to a topology of five themes that is derived from empirical research in the field. The five themes identified are structures, hierarchies, identity, flexibility and context specificity, and gradual relevance/subversion. Each theme explores a different facet of ‘doing gender’. This topology helps empirical researchers to be more specific about which aspects of ‘doing gender’ they are referring to. This in turn can help to unfold the theoretical potential of the concept of ‘doing gender’.     

Organising Generations – What Can Sociology Offer to the Understanding of Generations at Work?

Generations at work is a topic of much concern for research and practice, yet sociological scholarship on generations in a work context is scarce. This article brings together sociological research on generations with popular and psychological research on generations at work and explores the potential of a sociology of generations at work through a discussion of the latest generation entering the workplace. The article proceeds to explore which contribution a sociology of generations at work might make and suggests some potential avenues for further research.     

Asteroid Risk Assessment: A Probabilistic Approach

Following the 2013 Chelyabinsk event, the risks posed by asteroids attracted renewed interest, from both the scientific and policy‐making communities. It reminded the world that impacts from near‐Earth objects (NEOs), while rare, have the potential to cause great damage to cities and populations. Point estimates of the risk (such as mean numbers of casualties) have been proposed, but because of the low‐probability, high‐consequence nature of asteroid impacts, these averages provide limited actionable information. While more work is needed to further refine its input distributions (e.g., NEO diameters), the probabilistic model presented in this article allows a more complete evaluation of the risk of NEO impacts because the results are distributions that cover the range of potential casualties. This model is based on a modularized simulation that uses probabilistic inputs to estimate probabilistic risk metrics, including those of rare asteroid impacts. Illustrative results of this analysis are presented for a period of 100 years. As part of this demonstration, we assess the effectiveness of civil defense measures in mitigating the risk of human casualties. We find that they are likely to be beneficial but not a panacea. We also compute the probability—but not the consequences—of an impact with global effects (“cataclysm”). We conclude that there is a continued need for NEO observation, and for analyses of the feasibility and risk‐reduction effectiveness of space missions designed to deflect or destroy asteroids that threaten the Earth.     

Risk and Uncertainty Analysis in Government Safety Decisions

Probabilistic risk analysis (PRA) can be an effective tool to assess risks and uncertainties and to set priorities among safety policy options. Based on systems analysis and Bayesian probability, PRA has been applied to a wide range of cases, three of which are briefly presented here: the maintenance of the tiles of the space shuttle, the management of patient risk in anesthesia, and the choice of seismic provisions of building codes for the San Francisco Bay Area. In the quantification of a risk, a number of problems arise in the public sector where multiple stakeholders are involved. In this article, I describe different approaches to the treatments of uncertainties in risk analysis, their implications for risk ranking, and the role of risk analysis results in the context of a safety decision process. I also discuss the implications of adopting conservative hypotheses before proceeding to what is, in essence, a conditional uncertainty analysis, and I explore some implications of different levels of "conservatism" for the ranking of risk mitigation measures.