International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

Safety Culture describes how safety issues are managed within an enterprise. How to make safety culture strong and sustainable? How to be sure that safety is a prime responsibility or main focus for all types of activity? How to improve safety culture and how to identify the most vulnerable issues of safety culture? These are important questions for safety culture. Huge amount of studies focus on identifying and building the hierarchy of the main indicators of safety culture. However, there are only few methods to assess an organization's safety culture and those methods are often straightforward.

In this paper we describe a novel approach for safety culture assessment by using Belief Degree-Distributed Fuzzy Cognitive Maps (BDD-FCMs). Cognitive maps were initially presented for graphical representation of uncertain causal reasoning. Later Kosko suggested Fuzzy Cognitive Maps FCMs in which users freely express their opinions in linguistic terms instead of crisp numbers. However, it is not always easy to assign some linguistic term to a causal link. By using BDD-FCMs, causal links are expressed by belief structures which enable getting the links evaluations with distributions over the linguistic terms. In addition, we propose a general framework to construct BDD-FCMs by directly using belief structures or other types of structures such as intervals, linguistic terms, or crisp numbers. The proposed framework provides a more flexible tool for causal reasoning as it handles different structures to evaluate causal links.

Enterprise Risk Management (ERM) is a framework that is used by large organizations to manage risk as a whole. The key difference between ERM and traditional risk management is that in the latter risks are managed individually, whilst the former requires the aggregation of risks to facilitate risk management. However, current methods for risk aggregation have various limitations when applied under the context of ERM, such as the requirement for accurate and complete information about risk factors, the inability to handle different kinds of uncertainty which are inevitable during the risk aggregation process, and so on. Due to its unique advantages in accommodating different forms of both complete and incomplete information and handling different kinds of uncertainty, the Evidential Reasoning (ER) approach together with its implementation entitled Intelligent Decision System (IDS) is introduced in this paper for risk aggregation in ERM to overcome the limitations and to provide a comprehensive analysis for risk management based on the aggregation result. To demonstrate the applicability of the ER approach and IDS in ERM, a case study is analyzed in detail regarding risk aggregation and risk management for a health care organization in North England.

Public transportation can be viewed as a key determinant and consequence of the social and spatial formation and development of contemporary cities and regions. Transportation policy generally combines four categories of instruments, i.e., investment, pricing, regulation and subsidy, to generate viable alternatives. Capital investment in public transportation supports the purchase of equipment and facilities including rolling stock, tracks, control equipment, and the construction of terminals, stations, parking lots, maintenance facilities and power generating facilities. Risk governance for public transportation investments looks at how risk-related decision-making unfolds when a range of actors is involved, requiring co-ordination and possibly reconciliation between a profusion of roles, perspectives, goals and activities. In this paper, a two-phased multicriteria methodology is proposed to select the best investment alternative for public transportation with respect to the predetermined criteria. In the first phase, a selection among transportation types is made, and in the second phase, a selection among transportation modes of the selected transportation type is made. A case study for Istanbul is given in the application section.

Cooperative game theory is very useful to risk aversion problems in economics and management systems. The existing methods only focus on the situation payoffs take the form of numerical values, ones take the form of linguistic labels are seldom discussed. The aim of this study is to propose the consistent imputation for cooperative games under a linguistic environment. To support risk aversion, a 2-tuple linguistic representation is employed to obtain the valid results and avoid the loss of linguistic information. This paper firstly defines some concepts for linguistic cooperative games, such as linguistic imputation, carrier, core and null player. A set of their desirable properties are also discussed. The linguistic Shapley value is then presented based on three axioms. Moreover, the existence and uniqueness of the linguistic Shapley value are discussed in detail. To adjust the linguistic imputation in accordance with the cardinality of a given original linguistic label set, an adjustment algorithm for generating consistent imputation is proposed. Finally, we give the application of linguistic imputation in solving risk aversion problems to illustrate the validity of the consistent imputation generation (CIG) method.

The urban rail system in Istanbul carries in total more than 700.000 passengers per a day on different types of lines which require well organized risk governance. This paper evaluates the urban rail systems in Istanbul under different risk factors using Fuzzy Analytic Hierarchy Process (FAHP) to uncover the critical risk criteria of these systems and to make a multi-criteria evaluation of existing rail systems for the assignment of the scarce resources. Linguistic variables are used in the pairwise comparisons of criteria and alternatives. The risk factors considered are regional criticality, line characteristics, line safety and station structure. The evaluation results imply that the most risky critical urban rail system in Istanbul is the subway line from Sishane to Darussafaka.

Performance, reliability and safety are relevant factors when analyzing or designing a computer system. Many studies about on performance are based on monitoring and analyzing data from a computer system. One of the most useful pieces of data is the Load Average (LA) that which shows the load average of the system in the last minute, the sequence of in the last five minutes and the sequence of in the last fifteen last minutes. There are a lot ofmany studies of the system performance based on the load average. This is shown by mean means of monitoring the commands of the operative system, but sometimes they are sometimes difficult to understand and far of removed from human intuition. The aim of this paper is to show demonstrate a new procedure that allows us to determine the stability of a computer system from a list of load average sample data. The idea is shown as an algorithm based in statistic analysis, the aggregation of information and its formal specification. The result is an evaluation of the stability of the load and the computer system by monitoring but without adding any overhead to the system. In addition, the procedure can be used as a software monitor for risk prevention of on any vulnerable system.

The aim of this paper is to propose a new measure approach of ambiguous risk aversion under some capacity μ (a non-additive measure), in particular, under the distorted probability. Firstly, by using the Choquet integral with respect to the capacity μ, we introduce the concept of ambiguous risk premium of a risk asset X for risk aversive individuals whose utility function is u, and we investigate some properties of the ambiguous risk premium under some assumptions. Then to illustrate our theoretical results, we give an example and empirical results.

This paper focuses on an inference methodology based on a belief linguistic rule base (B-LRB) for qualitative decision support. It is termed 'linguistic rule-base' instead of 'fuzzy rule-base' because the use of membership functions associated with the linguistic terms are unnecessary or do not play a key role. The features of B-LRB, the ways to generate a B-LRB, and the inference procedure based on B-LRB are specified, along with an illustrate example applied to evaluate consumer trustworthiness in Internet marketing to show how it works, its applicability and feasibility.

By considering the risks in policy making procedure, a three-way decision approach based on the decision-theoretic rough set model is adopted to risk government decision-making. A three-way decision is made based on a pair of thresholds on conditional probabilities. A positive rule makes a decision of executing, a negative rule makes a decision of non-executing, and a boundary rule makes a decision of deferment. The loss functions are used to calculate the required two thresholds to describe the decision risk with the Bayesian decision procedure. A case study of government petroleum risk investment demonstrates the proposed method.

One of the key issues for support fuzzy decision-making is fuzzy number ranking. The existing ranking methods either do not provide a total ordering or cannot be effectively applied to decision-making processes. In this paper, we first give five basic principles that interval number ranking must satisfy, and construct a quantitative ranking model of interval numbers based on the synthesis effects of each index. We then propose a new constructions method of synthesis effect function systematically. Third, we also develop a new fuzzy numbers ranking model based on numerical characteristics, combining with the interval representation theorem of fuzzy numbers, and analyze the performance and characteristics of this ranking method by a case-based example. The results indicate that this proposed ranking method has good operability and interpretability, which can integrate the decision consciousness into decision process effectively and serve as a guideline for constructing different fuzzy decision methods.

Identifying key members from web-based social networks assists in assessing the risk of criminal network formation. To manage the uncertainty in complex web-based social networks, we first formally defined the binary relation and uncertainty of pages in web-based social networks. Secondly, we proposed an effective algorithm for Mining Key member from uncertain web-based social networks, called MiKey, by integrating uncertainty of pages into three centrality measures including degree, betweenness, and closeness. MiKey takes into a full consideration of the uncertainty in web-based social networks by computing the transition probability from one page to another. Furthermore, we briefly introduced the approach of calculating the k-order transition matrix of pages. Finally, we conducted experiments on real web data and the results show that MiKey is effective in discovering key pages from web-based social networks with less time deficiency than the centrality measures based algorithm.

The evaluation of sustainable energy policies supports the selection of the best policy to put it in practice. In this evaluation, stakeholders may express their preferences in different domains, considering their diverse background and the imprecision and uncertainty of the related information, as well as the nature of assessed criteria. Therefore, these evaluation problems require the selection of an adequate approach to manage such a heterogeneous framework. In this paper, we review three approaches with different strategies to deal with heterogeneous information and apply them to the evaluation of sustainable energy policies, with the view of analyzing their influence in a complex evaluation process, mainly in terms of interpretability and understandability.