An Optimization‐Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi‐level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability‐based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi‐level max‐min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one‐level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more‐informed prehazard preparation decisions.     

Exploring the relationship between product-service system and profitability

Journal of Management and Governance - Over the last few decades there has been a significant change in the economic output worldwide, and services have become more and more important....     

Bayesian Conditional Mean Estimation in Log‐Normal Linear Regression Models with Finite Quadratic Expected Loss

Log‐normal linear regression models are popular in many fields of research. Bayesian estimation of the conditional mean of the dependent variable is problematic as many choices of the prior for the variance (on the log‐scale) lead to posterior distributions with no finite moments. We propose a generalized inverse Gaussian prior for this variance and derive the conditions on the prior parameters that yield posterior distributions of the conditional mean of the dependent variable with finite moments up to a pre‐specified order. The conditions depend on one of the three parameters of the suggested prior; the other two have an influence on inferences for small and medium sample sizes. A second goal of this paper is to discuss how to choose these parameters according to different criteria including the optimization of frequentist properties of posterior means.     

The Effect of Cultural Capital on High School Dropout: An Investigation in the Italian Provinces

This article studies at a detailed geographical level the relation between cultural capital and high school dropout. Bronfenbrenner’s systemic theory and Heckman’s perspective on cognitive/non-cognitive skills are considered as theoretical framework. We analyzed data from 103 Italian provinces employing Covariance Structure Analysis and spatial indices of autocorrelation. We found a consistent protective effect of cultural capital on dropout, independently of economic performance, in Central and Southern provinces, but not in Northern provinces. Spatial analyses showed very heterogeneous patterns of autocorrelation for dropout (especially across Southern provinces) even between neighboring areas, in spite of a more compact clustering when considering cultural and economic indicators. These results indicate that living in an environment with animated cultural life might enhance students’ non-cognitive skills, thus fostering their involvement in formative activities and the development of their human capital.     

The MIMIC–CUB Model for the Prediction of the Economic Public Opinions in Europe

Social Indicators Research - To study the Europeans’ perception on the economic conditions, a model that combine Multiple Indicators Multiple Causes (MIMIC) and Combination of Uniform and...     

The Construction of a Synthetic Index Comparing Multidimensional Well-Being in the European Union

For many years, the quantification and measurement of level of well-being in a society has become an object of study by researchers, economists, international organizations and institutions. The purpose of these researches and applications is mainly the collection of data as accurate and complete as possible, dictating the paths of economic and social development policies, in order to help the economic problem of allocating scarce resources within a community, where not all individual needs can be fully met. The present work is intended as a part of that field. It will undertake the construction of a composite index of multidimensional well-being, through an aggregation of data, able to balance the trade-off between immediacy and completeness of information and to trespass the limits that characterize the commonly used income related measures. The method of factor analysis, which aims at detecting a statistically sufficient number of variables, is used to represent most of the explained variance of the phenomenon. Results are tested with different aggregative processes. This analysis is applied to the reality of the European Union, characterized by deep transformation and cultural, economic and social inequalities.     

Human Reliability Analysis by Fuzzy "CREAM"

This article proposes a systematic procedure for computing probabilities of operator action failure in the cognitive reliability and error analysis method (CREAM). The starting point for the quantification is a previously introduced fuzzy version of the CREAM paradigm that is here further extended to account for: (1) the ambiguity in the qualification of the conditions under which the action is performed (common performance conditions, CPCs) and (2) the fact that the effects of such conditions on human performance reliability may not all be equal.     

Eliciting decision weights by adapting de Finetti’s betting-odds method to prospect theory

This paper extends de Finetti’s betting-odds method for assessing subjective beliefs to ambiguous events. Thus, a tractable manner for measuring decision weights under ambiguity is obtained. De Finetti’s method is so transparent that decision makers can evaluate the relevant tradeoffs in complex situations. The resulting data can easily be analyzed, using nonparametric techniques. Our extension is implemented in an experiment on predicting next-day’s performance of the Dow Jones and Nikkei stock indexes, where we test the existence and nature of rank dependence, finding usual patterns. We also find violations of rank dependence.

When the main job tasks are perceived to be ‘irrelevant’ in the workplace: the internal uselessness of corporate social responsibility work in Japan

ABSTRACT

Despite a symbolic shift toward ‘emotional taint’ in dirty work literature, the role of the workplace has not been studied in relation to socially admired professions, such as corporate social responsibility (CSR) work. This article carries out an in-depth, critical examination of CSR as an emotionally tainted occupation in Japan. Its findings, substantiated by an analysis of 34 CSR workers’ rhetoric, help conceptualise ‘internal uselessness’. This emerges when workers feel their organisations publicly foster an image of their work that is decoupled from its internal reality, assigning them chief tasks they consider irrelevant. This leads to negative consequences that damage their workplace social relationships, professional aspirations and emotional well-being. The findings ultimately show that these CSR workers in Japan attempt to counterbalance internal uselessness through a social quest with peers outside their workplace, but also manage their emotions by rationalising their job status as inescapable, influenced by situated commitment norms.     

Assessing the Performance of a Classification‐Based Vulnerability Analysis Model

In this article, a classification model based on the majority rule sorting (MR‐Sort) method is employed to evaluate the vulnerability of safety‐critical systems with respect to malevolent intentional acts. The model is built on the basis of a (limited‐size) set of data representing (a priori known) vulnerability classification examples. The empirical construction of the classification model introduces a source of uncertainty into the vulnerability analysis process: a quantitative assessment of the performance of the classification model (in terms of accuracy and confidence in the assignments) is thus in order. Three different app oaches are here considered to this aim: (i) a model–retrieval‐based approach, (ii) the bootstrap method, and (iii) the leave‐one‐out cross‐validation technique. The analyses are presented with reference to an exemplificative case study involving the vulnerability assessment of nuclear power plants.