The use of expert systems can be helpful to improve the transparency and repeatability of assessments in areas of risk analysis with limited data available. In this field, Human Reliability Analysis (HRA) is no exception, and, in particular, dependence analysis is an HRA task strongly based on analyst judgement. The analysis of dependence among Human Failure Events refers to the assessment of the effect of an earlier human failure on the probability of the subsequent ones. This paper analyses and compares two expert systems, based on Bayesian Belief Networks and Fuzzy Logic (a Fuzzy Expert System, FES), respectively. The comparison shows that a BBN approach should be preferred in all the cases characterized by quantifiable uncertainty in the input (i.e. when probability distributions can be assigned to describe the input parameters uncertainty ), since it provides a satisfactory representation of the uncertainty and its output is directly interpretable for use within PSA. On the other hand, in cases characterized by very limited knowledge, an analyst may feel constrained by the probabilistic framework, which requires assigning probability distributions for describing uncertainty. In these cases, the FES seems to lead to a more transparent representation of the input and output uncertainty.
ne of the challenges in Risk Analysis and Management (RAM) is identifying the relationships between risk factors and risks. The complexity of the method to analyze these relationships, the time to complete the analysis, the robustness and trustworthiness of the method are important features to be considered. In this paper, we propose using Extended Fuzzy Cognitive Maps (E-FCMs) to analyze the relationships between risk factors and risks, and adopting a pessimistic approach to assess the overall risk of a system or a project. E-FCMs are suggested by Hagiwara to represent causal relationships more naturally. The main differences between E-FCMs and conventional Fuzzy Cognitive Maps (FCMs) are the following: E-FCMs have nonlinear membership functions, conditional weights, and time delay weights. Therefore E-FCMs are suitable for risk analysis as all features of E-FCMs are more informative and can fit the needs of Risk Analysis. In this paper we suggest a framework to analyze risks using E-FCMs and extend E-FCMs themselves by introducing a special graphical representation for risk analysis. We also suggest a framework for group decision making using E-FCMs. Particularly, we explore the Software Project Management (SPM) and discuss risk analysis of SPM applying E-FCMs
Both the natural and the social sciences are currently facing a deep "reproducibility crisis". Two important factors in this crisis have been the selective reporting of results and methodological problems. In this article, we examine a fusion of these two factors. More specifically, we demonstrate that the uncritical import of Boolean optimization algorithms from electrical engineering into some areas of the social sciences in the late 1980s has induced algorithmic bias on a considerable scale over the last quarter century. Potentially affected are all studies that have used a method nowadays known as Qualitative Comparative Analysis (QCA). Drawing on replication material for 215 peer-reviewed QCA articles from across 109 highprofile management, political science and sociology journals, we estimate the extent this problem has assumed in empirical work. Our results suggest that one in three studies is affected, one in ten severely so. More generally, our article cautions scientists against letting methods and algorithms travel too easily across disparate disciplines without sufficient prior evaluation of their suitability for the context in hand.
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