An evaluation of the role of fuzzy cognitive maps and Bayesian belief networks in the development of causal knowledge systems

Wee, Yit Yin; Cheah, Wooi Ping; Tan, Shing Chiang; Wee, Kuok Kwee

doi:10.3233/jifs-179252

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Causal-based and model-driven systems such as FCMs and Bayesian belief networks (BBNs) are based on visual graphs consisting of nodes (variables) and directional links between nodes that represent cause-andeffect relationships between the variables. e research findings show that in comparison to BBNs, FCMs are more suitable for use as a front-end modeling tool to elicit expert knowledge, since the causal model is simpler, more intuitive, and user-friendly, making easier their composition and decomposition [42,43].…”

Section: Introductionmentioning

confidence: 99%

Improved Decision-Making through a DEMATEL and Fuzzy Cognitive Maps-Based Framework

Mazzuto

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Ciarapica

et al. 2022

Mathematical Problems in Engineering

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The decision-making process is highly demanding. There has been an increasing tendency to incorporate human thinking, individual experience about a problem, and pure mathematical approaches. Here, a novel integrated approach is investigated and proposed to develop an advanced hybrid decision-support system based on the decision-making trial and evaluation laboratory (DEMATEL) and fuzzy cognitive maps (FCMs). Indeed, knowledge acquisition and elicitation may present distortions and difficulties finding a consensus and an interpretation. Thus, the proposed combined approach aims to examine in depth the potential to improve FCMs’ outcomes by integrating FCM with the DEMATEL approach. The combined methodology achieves at avoiding some of the drawbacks, such as the lack of a standardized FCM theoretical model. Thus, it provides advanced comparative analysis and results in better interpretation of the decision-making process. It is highlighted that the traditional FCM approach does not allow distinguishing the whole number of defined scenarios, in contrast to the hybrid one presented here, which increases the ability of users to make correct decisions. Combining the two approaches provides new capabilities to FCMs in grouping experts’ knowledge, while the DEMATEL approach contributes to refining the strength of concepts’ connections.

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Section: Introductionmentioning

confidence: 99%

Improved Decision-Making through a DEMATEL and Fuzzy Cognitive Maps-Based Framework

Mazzuto

Stylios

Ciarapica

et al. 2022

Mathematical Problems in Engineering

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“…Özesmi and Özesmi [11] created FCM based on the perceptions of different stakeholders in real environment management to facilitate the development of participatory environmental management plans. Wee et al [12] compared the different roles of BBN and FCM in the development of causal knowledge system, and showed that FCM was in general far superior to BBN in terms of understandability, usability, modularity, and scalability. On the other hand, in terms of expressiveness and inferential capability, BBN is in general far superior to FCM.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Cognitive Maps-Based Switched-Mode Power Supply Design Assistant System

et al. 2020

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Traditional engineering design approaches primarily solve technical problems and often ignore the importance of human factors. To reduce human errors and workload in power electronics, this paper proposes a switched-mode power supply design (SMPS) assistant system based on Fuzzy Cognitive Maps (FCMs). This system incorporates both technical requirements and human factors that involve designers' knowledge and skills in the SMPS design domain. First, we identify the critical concepts from power management lab kits and power electronics books, and extract latent sub-skills of SMPS design using exploratory factor analysis to build the starting concept list of FCM. Second, we use factor analysis and correlation analysis to determine the causal weights between the captured components to build initial FCM based on the starting concept list of FCM. Third, through interviews with subject-matter experts, we get their inputs on the initial main map and capture their individual FCMs. Then, we integrate experts' individual FCMs with different weights. After that, we determine the degree of fuzzification of the threshold function through analyzing data collected based on the prediction results of the only decision concept in the proposed FCM-SMPS quality. Two WHAT-IF scenarios are analyzed based on different inputs using the FCM Expert tool. The scenario test results provide guidances to designers in terms of knowledge or skills improvements and power supply debugging. Finally, we evaluate the proposed system using eight scenarios. The evaluation results of components' actual states are consistent with their preferred states, which suggests that the proposed FCM-based assistant system is reliable and effective.

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An evaluation of the role of fuzzy cognitive maps and Bayesian belief networks in the development of causal knowledge systems

Cited by 2 publications

References 23 publications

Improved Decision-Making through a DEMATEL and Fuzzy Cognitive Maps-Based Framework

Improved Decision-Making through a DEMATEL and Fuzzy Cognitive Maps-Based Framework

Fuzzy Cognitive Maps-Based Switched-Mode Power Supply Design Assistant System

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