Integrating human attitude in risk assessment process

International audienceThe notion of risk is a pervasive one nowadays, due probably to the fact that, though the humanity is living in an advanced technology era, it is not fully protected from catastrophic events, be them natural or resulting from human activities; even this advanced technology is creating new treats for the humanity. Thus, to avoid extreme reactions with regards to these risks and in order to be armed to manage efficiently these risks in terms of resources allocation, the notion of risk must be addressed scientifically when taking into account social, cultural, moral, philosophical, psychological attributes of human beings. This paper aimed to establish a framework that permits to analyze a system (physical objects such as an infrastructure, production plant; organizational system; managerial system, etc.) with regards to risk it may face. Two main issues are addressed in the paper: structuring risks identification and impact assessment process and measuring risk taking into account the nature of the system under review and stakeholders or decision makers attitude towards risk for the purpose of resources allocation for instance. To be sound and robust, a structuring approach must rely on some concepts; in this paper, bipolarity, in the sense that facing an adverse event a system will exhibit resistant as well as weak aspects, will be the underlying structuring concept

A Model to Support Risk Management Decision-Making

This paper considers the issue of designing a framework to efficiently manage the risk due to some adverse events an organization or a system may face. Risk comes from human being’s incapacity to predict the consequences or outcomes of some external events and/or their own actions, or to express precisely their knowledge about things. Thus, risk is linked to uncertainties that are inherent to almost all activities of human being. Designing an effective risk management decision making framework necessitate to correctly address these uncertainties in terms of appropriate mathematical tools along with procedures to identify variables (risk factors, state of the system, consequences, objectives or stakes, possible actions, etc.) impacting decision process and relationships linking them and finally aggregating approaches to present high level managers with concise information. In this paper we will use a meta-matrix analysis to identify relationships between previously determined variables, Bayesian networks and influence diagrams, graphical tools that permit easy representation of probabilistic relationships (independence, causality, correlation, etc.) between variables to quantify these relationships, and Choquet integral as an aggregation tool

Modeling dynamic reliability using dynamic Bayesian networks

This paper considers the problem of modeling and analyzing the reliability of a system or a component (system) where the state of the system and the state of process variables influences each other in addition to an exogenous perturbation influence: this is the dynamic reliability. We consider discrete time case, that is the state of the system as well as the state of process variables are observed or measured at discrete time instants. A mathematical tool that shows interesting properties for modeling and analyzing this problem is the so called Dynamic Bayesian Networks (DBN) that permit graphical representation of stochastic processes. Furthermore their learning and inference capabilities can be exploited to take into account experimental data or expert’s knowledge. We will show that a complex interaction between system and process on one hand and between system, process and exogenous perturbation on the other hand can simply be represented graphically by a dynamic Bayesian network. With their extended tool, known as influence diagrams (ID) that integrate actions or decisions possibilities, one can analyze and optimize a maintenance policy and/or make reactive decision during an accident by simulating different scenarios of its evolution for instance

Evaluation model for multiattributes-multiagents decision making: satisficing game approach

This paper considers the evaluation step in a decision-making process that follows decision-making goals setting, feasible alternatives and attributes or criteria that characterize them determination steps. Evaluation step must establish a model or algorithm to evaluate alternatives taking into account their performances with regard to criteria as well as decision makers or stakeholders preferences. Though this problem is rather a classic one, researches related to evaluation model construction continue to be active to find models that cope with more realities or that fit well how human beings behave in group and proceed when facing the problem of choosing, ranking or sorting alternatives or options. The purpose of this paper is to construct an evaluation model that integrate the performances of alternatives with regard to attributes or criteria and decision makers or agents opinions with regard to the importance to assign to each criterion in order to obtain a value function. As any decision problem is almost always a matter of tradeoff, among attributes characterizing alternatives there will be those acting toward the achievement of decision makers goal (benefit) and those that decision makers would like to reduce as much as possible (cost); we will designate the first ones as positive attributes and the later ones as negative attributes. The process of dividing attributes into positive attributes and negative attributes is beyond the scope of this paper and this partition will be considered as a part of the problem specification. The model is constructed in two steps: firstly, satisfiability (selectability and rejectability) measures or functions are obtained for each alternative using attributes values (positive attributes will contribute to selectability measure whereas negative ones are used in the derivation of rejectability measure) and agents opinions in the framework of satisficing game theory and secondly a value function is built on that measures. Agents opinions with regard to attributes will be expressed locally by weighting them by category (positive/negative)

BOCR framework for decision analysis

This paper considers establishing a framework for modellig decision analysis problems where the analyst must cope with uncertainty, multiple objectives, multiple attributes and multiple actors. These probems rise when considering large scale and complex decision problems encountered in real world applications in domains such as risk assessment and managment, infrastrucutres planning, complex process monitoring, supply chain planning, etc. To tackle this modelling chalenges, we propose to use BOCR (benefit, opportunity, cost, and risk) paradigm to identify attributes that must characterize an alternative with regard to a given objective. Then Bayesian network and/or AHP (analytic hierrarchy process) analysis can be used to assess the values of these later attributes. Finally an aggregation method based on satisficing game is developed that permit to evaluate each alternative by two measures: selectability degree constructed using “positive” attributes (benefit and opportunity) and the rejectability degree built on “negative” attributes (cost and risk)

Bipolar aggregation method for fuzzy nominal classification using Weighted Cardinal Fuzzy Measure (WCFM)

The issue of designing a procedure to assign objects (candidates, projects, decisions, options, etc.) characterized by multiple attributes or criteria to predefined classes characterized by fuzzyly defined multiple features, conditions or constraints, is considered in this paper. Such assignment problems are known in the literature as nominal or non ordered classification problems as opposed to ordinal classification in which case classes are ordered according to some desires of decision maker(s). Because of the importance of these problems in many domains such as social, economics, medical, engineering, mangement etc., there is a need to design sound and appropriate evaluation algorithms and methods to deal with them. In this paper we will consider an approach based on an evaluation strategy that consists in aggregating separately elements that act in the same sens (either contributing to the exlusion of a class from assignment or its consideration for inclusion given an object) that we refer to as bipolar analysis. Then, relying on the fact that elements to aggregate have synergetic relationships (they are complementary), we propose to use Choquet integral as the appropriate aggregation operator with a proposed fuzzy measure or capacity known as weighted cardinal fuzzy measure (WCFM) which tractability permits to overcome dificulties that dissuade the use of Choquet integral in practices. Furthermore, bipolar property results in evaluation by two degrees: classifiability measure that measures to what extent an object can be considered for inclusion in a class and rejectability measure, a degree that measures the extent to which one must avoid including an object to a class rendering final choice flexible as many classes may be qualified for inclusion of an object. Application of this approach to a real world problem in the domain of banking has shown a real potentiality

Decision support system with uncertain data: Bayesian networks approach

This paper is concerned with decision support system (DSS) development for aid in decision-making with uncertain data. The decision-making problem is formulated in the framework of state-of-the-world decision-making model and the main purpose of the decision support system that will be developed is to aid to estimate the most likely state of the world according to some external or environmental events that are known to have an influence on it. The mathematical tool used to tackle this problem is bayesian networks (BN). The state of the world is considered to be a random variable depending on some random environmental events organized in probabilistic causal network known as bayesian network. Any evidence on environmental events is then propagated by the bayesian network to estimate the state of the world with some degree of confidence that is used to make decision

Modeling selecting and ranking alternatives characterized by multiple attributes to satisfy multiple objectives

A number of decision making problems consist in selecting and ranking alternatives (projects, candidates, policies, etc.) that are characterized by multiple attributes in order to satisfy multiple objectives. Furthermore, this process generally necessitate coping with many stakeholders opinion regarding the importance to assign to each attribute and/or each objective. Given an objective, there will be attributes that act in the sens of realization of this objective (supporting attributes), those working against the achievement of this objective (rejecting attributes) and finally some attributes may be neutral regarding the achievement of this objective. Building on such distinction of attributes, we propose in this paper an approach, based on satisficing game theory, that firstly determine satisficing alternatives, those alternatives for which the selectability measure (determined based on supporting attributes and stakeholders preferences) exeeds the rejectability mesaure (computed from rejecting attributes and stakeholders preferences) and secondly assign priorities to those satisficing alternatives so that an overall selectability exeeds an overall rejectability. An interesting thing to be noticed about this approach compared to existing ones is that it allows non homogeneity of attributes (all the alternatives do not need be characterized by the same attributes)

Modelling inoperability propagation mechanism in interdependent systems

This communication considers the issue of deriving a model to describ how the inoperating level of a particular system (a production unit, a transportation system, an energy supply plant, etc.) of interconnected or interdependent systems(neworking and interdependency are interchangeable in this communication) will impact the operating level of other systems for the purpose of analysis, simulation, prediction, risk assessment, etc. The mechanism of such impacting process may be very complex; for instance to impact the operating level of a system the inoperability of another system may need to reach a certain level (threshold), to combine (synergy) with other events or situations; there may exist some preemptivity condition (that is to destabilize a given system some particular conditions must be satisfied). The main purpose of this communication is therefore to establish a model of inoperability propagation in a networked systems when taking into account as much as possible phenomena such as thresholding, synergy, resilience, etc. Necessity of synergy appeals for a synergetic aggregation operator; to this end, we propose to consider using Choquet integral associeted with a weighted cardinal fuzzy meaure (wcfm) as the appropriate aggregation operator. Furthermore this association leads to a straightforward formula to compute the integral

A satisficing game theory approach for group evaluation of production units

The problem under consideration in this paper is that of analysing the performance of a production unit in two directions: resource utilization versus output perfomance on the one hand and inter-unit comparison (within-group evaluation) on the other hand, all this subjected to possible subjective intervention of a decision maker or group of decision makers (DMs). A well known method that deals mainly with the second point (without intervention of DMs) of this problem which is widely covered in the literature is the so called data envelopment analysis (DEA). The point of view that will be expressed in this paper can be thought of as complementary to the DEA approach giving a more complete analysis in terms of the weak points of units identification and DMs' recommendations. The performance of each decision unit is evaluated through the so called satisfiability functions in the framework of satisficing game theory