Fuzzy Cognitive Map

This survey work tries to review the most recent applications and trends on fuzzy cognitive maps (FCMs) at the last ten years. FCMs are inference networks, using cyclic directed graphs, for knowledge representation and reasoning. In the past decade, FCMs have gained considerable research interest and are widely used to analyze causal systems such as system control, decision making, management, risk analysis, text categorization, prediction etc. Some example application domains, such as engineering, social and political sciences, business, information technology, medicine and environment, where the FCMs emerged a considerable degree of applicability were selected Their dynamic characteristics and learning methodologies make them essential for modeling, analysis, prediction and decision making tasks as they improve the performance of these systems. A survey on FCM studies concentrated on FCM applications on diverse scientific fields is elaborated during the last decade.

Ces comportements doivent déterminer leurs réponses, en fonction des stimulus externes, mais aussi en fonction d'émotions internes. Nous proposons de décrire de tels comportements émotionnels à l'aide de cartes cognitives floues où ces états internes sont explicitement représentés. Nous détaillons comment les cartes cognitives floues permettent la spécification, le contrôle, la simulation interne et l'adaptation dynamique du comportement perceptif d'un animat. L'exécution parallèle et asynchrone du contrôle, de la simulation interne et de l'apprentissage débouche sur une architecture comportementale pour entités virtuelles autonomes. Nous illustrons notre démarche à travers un exemple académique non trivial de fiction interactive. ABSTRACT. We are interested here in the perceptive behaviors of autonomous virtual actors. These behaviors must determine their answers, not only according to the external stimuli, but also according to internal emotions. We propose to describe such emotional behaviors using fuzzy cognitive maps, where these internal states are explicitly represented. We detail how fuzzy cognitive maps allow the specification, the control, the internal simulation and the dynamic adaptation of the perceptive behavior of an animat. Their parallel and asynchronous execution leeds to the proposal of a behavioral architecture for virtual autonomous entities. All these are illustrated by the academic example of a non-trivial interactive fiction. MOTS-CLÉS : acteur virtuel, perception active, apprentissage, carte cognitive, architecture d'agent KEYWORDS: virtual actor, active perception, learning, cognitive map, agent architecture RSTI -TSI. Volume 24 -n • 10/2005, pages 1259 à 1293 1. Nous n'avons pas la prétention de fournir un modèle de stress ou de paranoïa ; nous utilisons ces termes psychologiques par simple analogie comportementale, dans un but pédagogique.

Facing the issues of structural complexity, on which stakeholders have different views, has increasingly led to the use of Soft Systems Methodology (SSM) in solving managerial problems. Moreover, the weaknesses of this methodology in considering all point of views and ensuring the effectiveness of the proposed changes have provided the motivation for applying Fuzzy Cognitive Map (FCM) in SSM. Using FCM as a modeling tool makes it possible to combine the views of different experts and form group FCM (GFCM). GFCM has the potential to be applied as a useful decision support tool in the stage of offering recommendations and changes. The methodology proposed in this article is applied to ticketing system of Raja passenger train company. This system, influenced by various policies and views, is analyzed with the recommended methodology and then the solutions for developing the system are suggested in a prioritized manner.

This paper elaborates on the application of Fuzzy Cognitive Maps (FCMs) in strategy maps (SMs). The limitations of the Balanced Scorecards (BSCs) and SMs are first discussed and analyzed. The need for simulated scenario based SMs is discussed and the use of FCMs as one of the best alternatives is presented. A software tool for the development, simulation and analysis of FCM based SMs is also presented. The effectiveness of the resulting software tool and FCM theory in SMs is experimented in two case studies in Banking.

The notion of n-best approximations can be used for error correction in coding theory. The notion of n-eigen values can be used in deterministic modal superposition principle for undamped structures, which can find its applications in finite element analysis of mechanical structures with uncertain parameters. Further, it is suggested that the concept of n-matrices can be used in real world problems which adopts fuzzy models like Fuzzy Cognitive Maps, Fuzzy Relational Equations and Bidirectional Associative Memories. The applications of these algebraic structures are given in the third chapter. The fourth chapter suggests problems to further a reader's understanding of the subject.

Business process reengineering (BPR) has made a significant impact on managers and academics. Despite the rhetoric surrounding BPR, articulated mechanisms, which support reasoning on the effect of the redesign activities to the performance of the business model, are still emerging. This paper describes an attempt to build and operate such a reasoning mechanism as a novel supplement to performance-driven change (PDC) exercises. This new approach proposes the utilization of the fuzzy causal characteristics of fuzzy cognitive maps (FCMs) as the underlying methodology in order to generate a hierarchical and dynamic network of interconnected performance indicators. By using FCMs, the proposed mechanism aims at simulating the operational efficiency of complex process models with imprecise relationships to quantify the impact of performance-driven reengineering activities. This research also establishes generic maps that supplement the strategic planning and business analysis phases of typical redesign projects in order to implement the integration of hierarchical FCMs into PDC activities. Finally, this paper discusses experiments with the proposed mechanism and comments on its usability.

In scenario planning, causal mapping has long been used as a means to elicit the worldviews of multiple experts, facilitate discussion, and challenge and improve mental models. Large and complex causal maps, however, are difficult to analyze. This paper proposes a novel method for scenario building, based on Fuzzy Cognitive Maps, that combines intuitive, cognitive mapping techniques with formal, quantitative analysis. The proposed method helps scenario planners to integrate the qualitative and partial knowledge of multiple individuals and overcome information processing limitations. The feasibility of the proposed approach is investigated with two scenario studies on solar photovoltaic panels.

The normative literature within the field of knowledge management has concentrated on techniques and methodologies for allowing knowledge to be codified and made available to individuals and groups within organizations. The literature on organizational learning, however, has tended to focus on aspects of knowledge that are pertinent at the macro-organizational level (i.e. the overall business). The authors attempt in this paper to address a relative void in the literature, aiming to demonstrate the interlocking factors within an enterprise information system that relate knowledge management and organizational learning, via a model that highlights key factors within such an inter-relationship. This is achieved by extrapolating data from a manufacturing organization using a case study, with these data then modeled using a cognitive mapping technique (fuzzy cognitive mapping, FCM). The empirical enquiry explores an interpretivist view of knowledge, within an information systems evaluation (ISE) process, through the associated classification of structural, interpretive and evaluative knowledge. This is achieved by visualizing inter-relationships within the ISE decision-making approach in the case organization. A number of decision paths within the cognitive map are then identified such that a greater understanding of ISE can be sought. The authors therefore present a model that defines a relationship between knowledge management (KM) and organizational learning (OL), and highlights factors that can lead a firm to develop itself towards a learning organization.

This study presents a survey on the most recent learning approaches and algorithms that are related to fuzzy cognitive maps (FCMs). FCMs are cognition fuzzy influence graphs, which are based on fuzzy logic and neural network aspects that inherit their main advantages. They gained momentum due to their dynamic characteristics and learning capabilities. These capabilities make them essential for modeling and decision-making tasks as they improve the performance of these tasks. An efficient number of learning algorithms for FCMs, by modifying the FCM weight matrix, have been developed in order to update the initial knowledge of human experts and/or include any knowledge from historical data in order to produce learned weights. The proposed learning techniques have mainly been concentrated on three directions: on the production of weight matrices on the basis of historical data, on adaptation of the cause-effect relationships of the FCM on the basis of experts' intervention, and on the production of weight matrices by combining experts' knowledge and data. The learning techniques could be categorized into three groups on the basis of the learning paradigm: Hebbian-based, populationbased, and hybrid, which subsequently combine the main aspects of Hebbian-based-and population-based-type learning algorithms. These types of learning algorithms are the most efficient and widely used to train the FCMs, according to the existing literature. A survey on recent advances on learning methodologies and algorithms for FCMs that present their dynamic capabilities and application characteristics in diverse scientific fields is established here.

This paper introduces a proposed enhanced model of fuzzy cognitive map (FCM) called dynamic fuzzy cognitive map (DFCM). The aim is to make both of fuzzification mapping and causal relationships between concepts are environment sensitive. Accordingly, knowledge representation and inference process will be more flexible, reliable, and dynamic especially when dealing with complex and dynamic environments. A special set of environment dominant variables (V) is proposed that represents the environment prevailing conditions. The behavior of DFCM depends on V. Hence, the same fuzzy cognitive map knowledge and concept values will infer deferent outcomes in response to changes in V. Accordingly, it will be very appropriate to apply DFCM when modeling a wider spectrum of real world problems in various fields like decision support systems, control systems, medical diagnoses systems, effect-based planning, and prediction systems.

This book introduces the concept of fuzzy super matrices and operations on them. This book will be highly useful to social scientists who wish to work with multi-expert models. Super fuzzy models using Fuzzy Cognitive Maps, Fuzzy Relational Maps, Bidirectional Associative Memories and Fuzzy Associative Memories are defined here. The authors introduce 13 multi-expert models using the notion of fuzzy supermatrices. These models are described with illustrative examples. This book has three chapters. In the first chaper, the basic concepts about super matrices and fuzzy super matrices are recalled. Chapter two introduces the notion of fuzzy super matrices adn their properties. The final chapter introduces many super fuzzy multi expert models.

Future planetary exploration of Titan will require higher degrees of on-board automation, including autonomous determination of sites where the probability of significant scientific findings is the highest. In this paper, a novel Artificial Intelligence (AI) method for the identification and interpretation of sites that yield the highest potential of cryovolcanic activity is presented. We introduce the theory of fuzzy cognitive maps (FCM) as a tool for the analysis of remotely collected data in planetary exploration. A cognitive model embedded in a fuzzy logic framework is constructed via the synergistic interaction of planetary scientists and AI experts. As an application example, we show how FCM can be employed to solve the challenging problem of recognizing cryovolcanism from Synthetic Aperture Radar (SAR) Cassini data. The fuzzy cognitive map is constructed using what is currently known about cryovolcanism on Titan and relies on geological mapping performed by planetary scientists to interpret different locales as cryovolcanic in nature. The system is not conceived to replace the human scientific interpretation, but to enhance the scientists' ability to deal with large amounts of data, and it is a first step in designing AI systems that will be able, in the future, to autonomously make decisions in situations where human analysis and interpretation is not readily available or could not be sufficiently timely. The proposed FCM is tested on Cassini radar data to show the effectiveness of the system in reaching conclusions put forward by human experts and published in the literature. Four tests are performed using the Ta SAR image (October 2004 fly-by). Two regions (i.e. Ganesa Macula and the lobate high backscattering region East of Ganesa) are interpreted by the designed FCM as exhibiting cryovolcanism in agreement with the initial interpretation of the regions by . Importantly, the proposed FCM is shown to be flexible and adaptive as new data and knowledge are acquired during the course of exploration. Subsequently, the FCM has been modified to include topographic information derived from SAR stereo data. With this additional information, the map concludes that Ganesa Macula is not a cryovolcanic region. In conclusion, the FCM methodology is shown to be a critical and powerful component of future autonomous robotic spacecraft (e.g., orbiter(s), balloon(s), surface/lake lander(s), rover(s)) that will be deployed for the exploration of Titan.

This paper proposes a new methodology for designing Fuzzy Cognitive Maps using crisp decision trees that have been fuzzified. Fuzzy cognitive map is a knowledge-based technique that works as an artificial cognitive network inheriting the main aspects of cognitive maps and artificial neural networks. Decision trees, in the other hand, are well known intelligent techniques that extract rules from both symbolic and numeric data. Fuzzy theoretical techniques are used to fuzzify crisp decision trees in order to soften decision boundaries at decision nodes inherent in this type of trees. Comparisons between crisp decision trees and the fuzzified decision trees suggest that the later fuzzy tree is significantly more robust and produces a more balanced decision making. The approach proposed in this paper could incorporate any type of fuzzy decision trees. Through this methodology, new linguistic weights were determined in FCM model, thus producing augmented FCM tool. The framework is consisted of a new fuzzy algorithm to generate linguistic weights that describe the cause-effect relationships among the concepts of the FCM model, from induced fuzzy decision trees.

Image Edge detection significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. This paper presents a fuzzy rule base algorithm which is capable of detecting edges efficiently from the gray scale images. The following paper introduces such operators on hand of computer vision application. In the proposed algorithm, edginess at each pixel of a digital image is calculated using three 33 linear spatial filters i.e. low-pass, high-pass and edge enhancement (Sobel) filters through spatial convolution process. Finally We Have compared results of the proposed algorithm with other algorithms such as Sobel, Robert, and Prewitt. Experimental results show the ability and high performance of proposed algorithm

Cognitive networks are proposed in the framework of evolution of network architectures as novel paradigms to provide autonomous in-network reasoning to support end-toend goals. While several approaches are available that propose different approaches, still the problem of reasoning represents a challenging issue. The paper aims at proposing a mathematical model, based on fuzzy cognitive maps, to support and provide a quantitative tool for implementing reasoning in nodes of a cognitive network. In particular, the idea is to provide a methodology to enable network nodes to represent the complex interactions that happen within their protocol stacks and across the network. The potential utility of the proposed scheme is then preliminarily validated on a sample scenario, outlining good results and relevant potential for future developments.

This paper is concerned with proposing a fuzzy cognitive map (FCM) driven approach for implementing expert decision support in the area of urban design. Previous research activities modeled a knowledge-based system using first order predicate calculus. The current approach utilizes the inherent analogies between SLD resolution trees and FCMs and proposes the generation of multi-branch map hierarchies in order to model the disjointed conjuncts of the logic-based rules. This research proposes specific algorithms for interpreting the logic-based rules to FCMs as well as specific algorithms and formulas for calculating the values of multi-branch map hierarchies. Finally, this paper presents preliminary experiments and comments on the usefulness of the proposed methodology tool. q

Fuzzy Cognitive Maps (FCMs) constitute an attractive knowledge-based methodology, combining the robust properties of fuzzy logic and neural networks. FCMs represent causal knowledge as a signed directed graph with feedback and provide an intuitive framework which incorporates the experts' knowledge. FCMs handle available information and knowledge from an abstract point of view. They develop behavioural model of the system exploiting the experience and knowledge of experts. The construction of FCMs is based mainly on experts who determine the structure of FCM, i.e. concepts and weighted interconnections among concepts. But this methodology may not be a sufficient model of the system because the human factor is not always reliable. Thus the FCM model of the system may requires restructuring which is achieved through adjustment the weights of FCM interconnections using specific learning algorithms for FCMs. In this article, two unsupervised learning algorithms are presented and compared for training FCMs; how they define, select or fine-tuning weights of the causal interconnections among concepts. The implementation and results of these unsupervised learning techniques for an industrial process control problem are discussed. The simulations results of training the process system verify the effectiveness, validity and advantageous characteristics of those learning techniques for FCMs. r

This paper explores the application of Fuzzy Cognitive Maps (FCM) to Failure Modes and Effects Analysis (FMEA). FMEAs are used in reliability and safety evaluations of complex systems to determine the effects of component failures on the system operation. FCMs use a digraph to show cause and effect relationships between concepts; thus, they can represent the causal relationships needed for the FMEA and provide a new strategy for predicting failure effects in a complex system.

The paper presents the generalized to the continuous type Petri nets and continuous fuzzy cognitive map and emphases: 1) the necessity to introduce continuous time dimension when practical interrelations in the real-world are modeled and investigated and 2) the common description for both models which up till now were considered as different. Simplified examples of modeling from the field of international relations are presented.

Cognitive maps have been used for analysing and aiding decision-making by investigating causal links among relevant domain concepts. A fuzzy cognitive map (FCM) is an extension of a cognitive map with the additional capability of representing feedback through weighted causal links. FCMs can be used as tools for both static as well as dynamic analysis of scenarios evolving with time. An FCM represents an expert's domain knowledge in a form that lends itself to relatively easy integration into a collective knowledge base for a group involved in a decision process. The resulting group FCM has the potential to serve as a useful tool in a group decision support environment. An appropriate methodology for the development and analysis of group FCMs is required. A framework for such a methodology consisting of the development and application phases is presented.

Fuzzy cognitive maps (FCMs) are a very convenient, simple, and powerful tool for simulation and analysis of dynamic systems. They were originally developed in 1980 by Kosko, and since then successfully applied to numerous domains, such as engineering, medicine, control, and political affairs. Their popularity stems from simplicity and transparency of the underlying model. At the same time FCMs are hindered by necessity of involving domain experts to develop the model. Since human experts are subjective and can handle only relatively simple networks (maps), there is an urgent need to develop methods for automated generation of FCM models. This study proposes a novel learning method that is able to generate FCM models from input historical data, and without human intervention. The proposed method is based on genetic algorithms, and requires only a single state vector sequence as an input. The paper proposes and experimentally compares several different design alternatives of genetic optimization and thoroughly tests and discusses the best design. Extensive benchmarking tests, which involve 200 FCMs with varying size and density of connections, performed on both synthetic and real-life data quantifies the performance of the development method and emphasizes its suitability.

The studies on the photovoltaic (PV) generation are extensively increasing, since it is considered as an essentially inexhaustible and broadly available energy resource. However, the output power induced in the photovoltaic modules depends on solar radiation and temperature of the solar cells. Therefore, to maximize the efficiency of the renewable energy system, it is necessary to track the maximum power point of the PV array. In this paper, a maximum power point tracker using fuzzy set theory is presented to improve energy conversion efficiency. A new method is proposed, by using a fuzzy cognitive network, which is in close cooperation with the presented fuzzy controller. The new method gives a very good maximum power operation of any PV array under different conditions such as changing insolation and temperature. The simulation studies show the effectiveness of the proposed algorithm.

Medical decision support systems can provide assistance in crucial clinical judgments, particularly for inexperienced medical professionals. Fuzzy cognitive maps (FCMs) is a soft computing technique for modeling complex systems, which follows an approach similar to human reasoning and the human decision-making process. FCMs can successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause and effect relationships among the concepts to model the behavior of any system. Medical decision systems are complex systems that can be decomposed to non-related and related subsystems and elements, where many factors have to be taken into consideration that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall clinical decision with a different degree. Thus, FCMs are suitable for medical decision support systems and appropriate FCM architectures are proposed and developed as well as the corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described. #

H. Papadopoulos, AS Andreou, and M. Bramer (Eds.): AIAI 2010, IFIP AICT 339, pp. 45–52, 2010. © IFIP International Federation for Information Processing 2010 ... Α Generic Tool for Building Fuzzy Cognitive Map ... Maria Papaioannou1, Costas Neocleous2, Anastasis ...

Factor or criteria prioritization is essential for decision making and planning. In most areas in decision making, integrating the related literature yields an exuberance of criteria which leads a robust decision. Yet, an excess number of criteria may handicap decision making or evaluations in terms of computational time and complexity. In these circumstances, decreasing the number of factors in exchange for a negligible amount of knowledge can emancipate the decision maker yet does not affect the quality of the decision. This elimination can be conducted through qualitative methods such as interviews or quantitative methods. However, quantitative methods are more trustworthy since qualitative methods can be deceptive due to the perceptions of the interviewee. Furthermore, working with larger groups is more prone to neutrality in terms of group thinking. On the subject of innovative power and risks, the literature offers 48 criteria depending on the industry, size or demographics of related companies. Prioritizing and working with these criteria for their decision making applications becomes computationally expensive, especially when embedded in more complex algorithms. In this study, 48 criteria will be reduced using Fuzzy Cognitive Maps and it is believed to provide a sufficient number of criteria with a negligible loss of information and comparisons will be conducted.

Medical decision making can be regarded as a process, combining both analytical cognition and intuition. It involves reasoning within complex causal models of multiple concepts, usually described by uncertain, imprecise, and/or incomplete information. Aiming to model medical decision making, we propose a novel approach based on cognitive maps and intuitionistic fuzzy logic. The new model, called intuitionistic fuzzy cognitive map (iFCM), extends the existing fuzzy cognitive map (FCM) by considering the expert's hesitancy in the determination of the causal relations between the concepts of a domain. Furthermore, a modification in the formulation of the new model makes it even less sensitive than the original model to missing input data. To validate its effectiveness, an iFCM with 34 concepts representing fuzzy, linguistically expressed patient-specific data, symptoms, and multimodal measurements was constructed for pneumonia severity assessment. The results obtained reveal its comparative advantage over the respective FCM model by providing decisions that match better with the ones made by the experts. The generality of the proposed approach suggests its suitability for a variety of medical decision-making tasks.

The abundance of data available under the smart grid paradigm makes it possible for the system operator to enhance the situational awareness across the power network. More accurate data logging and reporting, especially in the newly adopted object oriented based communication protocols such as IEC 61850, allows for more efficient analysis and interpretation of the state of the network. This paper explores one of such applications that arise from employment of the standard IEC 61850 in substations. It is proposed here that the integrity of the data available from across the substation can be verified using the quality and validity data available from different devices and control/protection functions in the substation. A fuzzy cognitive map is used here that derives relationships and associations between various functions within the substation and determines a level of confidence on the data available. Although this scheme is developed for a substation in this paper, it can be equally well applied to other applications beyond substations and across the distribution automation domain.

Fuzzy cognitive maps (FCMs) are recognized as a flexible and powerful modeling and simulating technique. However, it is a relatively new methodology, which exhibits weaknesses mainly in the algorithmic background. Such weaknesses become evident during heuristic evaluations of the cause-effect relationships describing FCM-based systems. External intervention (typically from experts) for the determination and fine-tuning of the FCM parameters cannot be regarded as an accurate and efficient way to design and manage FCMs, especially in the case of highly complicated structures, where even experts meet difficulties in their attempts at a holistic interpretation. The introduction and implementation of a training procedure based on a robust and flexible optimization tool constitutes a promising alternative. This study focuses on evolutionary computation, since this domain encompasses optimization techniques possessing the needed features for this type of problems. Evolution strategies (ESs...

Today, energy is an important part of the economic cycle of various countries in the international relations. Also, given the volatility of oil prices in world markets and its impact on the global economy, it is expected that world's gas industry and the use of gas as an alternative energy becomes more important. Therefore, study and research on supply and demand of energy, especially natural gas, and studying factors affecting energy consumption in various sectors is very important. In this research, first factors affecting the consumption of natural gas in the residential sector is investigated and identified, by interviewing experts of gas industry and through content analysis method, and then the causal model of direct and indirect relationships between the variables and their intensity of influence through fuzzy cognitive map is determined. So, for the first time qualitative cultural and social variables along with the climate, social and economic variables is included in causal modeling.

E-business has a significant impact on managers and academics. Despite the rhetoric surrounding e-business strategy formulation mechanisms, which support reasoning of the effect of strategic change activities to the maturity of the e-business models, are still emerging. This paper describes an attempt to build and operate such a reasoning mechanism as a novel supplement to e-business strategy formulation exercises. This new approach proposes the utilization of the fuzzy causal characteristics of Fuzzy Cognitive Maps (FCMs) as the underlying methodology in order to generate a hierarchical and dynamic network of interconnected maturity indicators. By using FCMs, this research aims at simulating complex strategic models with imprecise relationships while quantifying the impact of strategic changes to the overall e-business efficiency. This research establishes generic adaptive domains -maps in order to implement the integration of hierarchical FCMs into e-business strategy formulation activities. Finally, this paper discusses experiments with the proposed mechanism and comments on its usability.

This paper investigates a hybrid methodology that combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for modeling and controlling Supervisory Control Systems. A mathematical description of Fuzzy Cognitive Maps (FCM) will be presented and new construction methods will be extensively examined. A Fuzzy Cognitive Map will be developed to model and control a process example and the Supervisor-FCM model characteristics will be discussed. There is a going need for more autonomous and intelligent systems, especially in Complex Systems area, the application of Fuzzy Cognitive Maps for modeling the Supervisor may contribute in the development of more sophisticated systems.

Medical decision making can be regarded as a process, combining both analytical cognition and intuition. It involves reasoning within complex causal models of multiple concepts, usually described by uncertain, imprecise, and/or incomplete information. Aiming to model medical decision making, we propose a novel approach based on cognitive maps and intuitionistic fuzzy logic. The new model, called intuitionistic fuzzy cognitive map (iFCM), extends the existing fuzzy cognitive map (FCM) by considering the expert's hesitancy in the determination of the causal relations between the concepts of a domain. Furthermore, a modification in the formulation of the new model makes it even less sensitive than the original model to missing input data. To validate its effectiveness, an iFCM with 34 concepts representing fuzzy, linguistically expressed patient-specific data, symptoms, and multimodal measurements was constructed for pneumonia severity assessment. The results obtained reveal its comparative advantage over the respective FCM model by providing decisions that match better with the ones made by the experts. The generality of the proposed approach suggests its suitability for a variety of medical decision-making tasks.

Most decision-making processes are dynamic. Critical decisions require multiple and interrelated time-constrained decisions within strongly uncertain and so complex scenarios. The proposed methodology (mixing Delphi, Fuzzy Cognitive Maps and TOPSIS) and is a step forward with regard to the classic tools used in scenarios-based decision-support. This proposal allows managing uncertainty for improving scenarios-based decision-making. FCMs offer visual models for easier understanding by non-technical decision makers, because FCM can express human explicit and tacit knowledge. Moreover, FCM provide an intuitive, yet precise way of expressing concepts and reasoning about them at their natural level of abstraction. By transforming decision modelling into causal graphs, decision makers with no technical background can understand all of the components in a given scenario. In addition, scenarios are ranked using a multicriteria method (TOPSIS).

This paper addresses the problem of designing a knowledge management methodology tool to act as a decision support mechanism for geographically dispersed financial enterprises. The underlying research addresses the problem of information capture and representation in financial institutions in order to provide an implementation of the virtuous cycle of knowledge flow. The proposed methodology tool utilizes the fuzzy causal characteristics of Fuzzy Cognitive Maps (FCMs) to generate a hierarchical and dynamic network of interconnected financial performance concepts. By using FCMs, the proposed mechanism simulates the operational efficiency of distributed organizational models with imprecise relationships and quantifies the impact of the geographically dispersed activities to the overall business model. Generic adaptive maps that supplement the decision-making process present a roadmap for integrating hierarchical FCMs into the business model of typical financial sector enterprises. Copyright © 2004 John Wiley & Sons, Ltd.

Business process reengineering (BPR) has made a significant impact on managers and academics. Despite the rhetoric surrounding BPR, articulated mechanisms, which support reasoning on the effect of the redesign activities to the performance of the business model, are still emerging. This paper describes an attempt to build and operate such a reasoning mechanism as a novel supplement to performance-driven change (PDC) exercises. This new approach proposes the utilization of the fuzzy causal characteristics of fuzzy cognitive maps (FCMs) as the underlying methodology in order to generate a hierarchical and dynamic network of interconnected performance indicators. By using FCMs, the proposed mechanism aims at simulating the operational efficiency of complex process models with imprecise relationships to quantify the impact of performance-driven reengineering activities. This research also establishes generic maps that supplement the strategic planning and business analysis phases of typical redesign projects in order to implement the integration of hierarchical FCMs into PDC activities. Finally, this paper discusses experiments with the proposed mechanism and comments on its usability.

This paper addresses the problem of designing a knowledge management methodology tool to act as a decision support mechanism for geographically dispersed financial enterprises. The underlying research addresses the problem of information capture and representation in financial institutions in order to provide an implementation of the virtuous cycle of knowledge flow. The proposed methodology tool utilizes the fuzzy causal characteristics of Fuzzy Cognitive Maps (FCMs) to generate a hierarchical and dynamic network of interconnected financial performance concepts. By using FCMs, the proposed mechanism simulates the operational efficiency of distributed organizational models with imprecise relationships and quantifies the impact of the geographically dispersed activities to the overall business model. Generic adaptive maps that supplement the decision-making process present a roadmap for integrating hierarchical FCMs into the business model of typical financial sector enterprises. Copyright © 2004 John Wiley & Sons, Ltd.

E-business has a significant impact on managers and academics. Despite the rhetoric surrounding e-business strategy formulation mechanisms, which support reasoning of the effect of strategic change activities to the maturity of the e-business models, are still emerging. This paper describes an attempt to build and operate such a reasoning mechanism as a novel supplement to e-business strategy formulation exercises. This new approach proposes the utilization of the fuzzy causal characteristics of Fuzzy Cognitive Maps (FCMs) as the underlying methodology in order to generate a hierarchical and dynamic network of interconnected maturity indicators. By using FCMs, this research aims at simulating complex strategic models with imprecise relationships while quantifying the impact of strategic changes to the overall e-business efficiency. This research establishes generic adaptive domains -maps in order to implement the integration of hierarchical FCMs into e-business strategy formulation activities. Finally, this paper discusses experiments with the proposed mechanism and comments on its usability.

Despite the extensive research in human capital management and performance measurement, intelligent treasoning mechanisms, which integrate human resource (HR) practices into strategic-level shareholder decisions, are still emerging. This paper discusses a novel approach of designing a decision-modeling tool, which assesses the impact of contemporary human resource management (HRM) practices to the shareholder value and satisfaction. The underlying research addresses the problem of establishing HRM interrelationships in order to drive the overall business performance from the shareholder value perspective. The proposed methodology tool utilizes the fuzzy causal characteristics of fuzzy cognitive maps (FCMs) to generate a hierarchical and dynamic network of interconnected HR performance drivers. The intelligent computing characteristics of FCMs are also employed to establish a dynamic feedback and bi-directional alignment of HRM practices and strategic improvement. Finally, this research provides a practical insight on the applicability of soft approaches in capturing and illustrating the effect of HRM practices.

Maximum power point trackers (MPPTs) play an important role in photovoltaic (PV) power systems because they maximize the power output from a PV system for a given set of conditions, and therefore maximize the array efficiency. This paper presents a novel MPPT method based on fuzzy cognitive networks (FCN). The new method gives a good maximum power operation of any PV array under different conditions such as changing insolation and temperature. The numerical results show the effectiveness of the proposed algorithm.

Fuzzy Cognitive Maps (FCMs) are an efficient modeling method providing flexibility on the simulated system's design. They consist of nodes-concepts and weighted edges that connect the nodes and represent the cause and effect relationships among them. The performance of FCMs is dependent on the initial weight setting and architecture. This shortcoming can be alleviated and the FCM model can be enhanced if a fuzzy rule base (IF-THEN rules) is available. This research proposes a successful attempt to combine fuzzy cognitive maps with decision tree generators. A combined Decision Tree-Fuzzy Cognitive Map (DT-FCM) model is proposed when different types of input data are available and the behavior of this model is studied. In this research work, we introduce a new hybrid modeling methodology for decision making tasks and we implement the proposed methodology at a medical problem.

This paper addresses the problem of designing an "intelligent" decision support methodology tool to act as a back end to financial planning. The methodology tool proposes a novel approach to supplementing typical financial strategy formulation projects by utilizing the fuzzy causal characteristics of Fuzzy Cognitive Maps (FCMs) to generate a hierarchical and dynamic network of interconnected profit and loss (P&L) concepts. By using FCMs, the mechanism simulates the efficiency of complex hierarchical financial models with imprecise relationships and external stimuli while quantifying the impact of strategic changes to the overall P&L status. Generic maps that supplement the decision making process demonstrate a roadmap for integrating hierarchical FCMs into the P&L model of typical financial sector enterprises. Preliminary experiments indicate that ex ante reasoning of the impact of strategic changes (actual or hypothetical) to the status of financial performance can be effective and realistic, without employing detailed P&L numerical calculations.