Banking applications of FCM models

Miklós F. Hatwágner, Gyula Vastag, Vesa A. Niskanen, L. Kóczy

???family-name???: Chapter

Abstract

Fuzzy Cognitive Map (FCMs) is an appropriate tool to describe, qualitatively analyze or simulate the behavior of complex systems. FCMs are bipolar fuzzy graphs: their building blocks are the concepts and the arcs. Concepts represent the most important components of the system, the weighted arcs define the strength and direction of cause-effect relationships among them. FCMs are created by experts in several cases. Despite the best intention the models may contain subjective information even if it was created by multiple experts. An inaccurate model may lead to misleading results, therefore it should be further analyzed before usage. Our method is able to automatically modify the connection weights and to test the effect of these changes. This way the hidden behavior of the model and the most influencing concepts can be mapped. Using the results the experts may modify the original model in order to achieve their goal. In this paper the internal operation of a department of a bank is modeled by FCM. The authors show how the modification of the connection weights affect the operation of the institute. This way it is easier to understand the working of the bank, and the most threatening dangers of the system getting into an unstable (chaotic or cyclic state) can be identified and timely preparations become possible.

???language???English
???hostPublicationTitle???Studies in Computational Intelligence
???publisher???Springer Verlag
???pages???61-72
???numberOfPages???12
???DOIs???
???publicationStatus???Published - jan. 1 2019

???publicationSeries???

???publicationSeries.name???Studies in Computational Intelligence
???publicationSeries.volume???796
???publicationSeries.printISSN???1860-949X

Fingerprint

Large scale systems

Keywords

    ASJC Scopus subject areas

    • Artificial Intelligence

    Cite this

    Hatwágner, M. F., Vastag, G., Niskanen, V. A., & Kóczy, L. (2019). Banking applications of FCM models. ???in??? Studies in Computational Intelligence (???pages??? 61-72). (Studies in Computational Intelligence; ???volumeLabel??? 796). Springer Verlag. https://doi.org/10.1007/978-3-030-00485-9_7

    Banking applications of FCM models. / Hatwágner, Miklós F.; Vastag, Gyula; Niskanen, Vesa A.; Kóczy, L.

    Studies in Computational Intelligence. Springer Verlag, 2019. ???pages??? 61-72 (Studies in Computational Intelligence; ???book.volume??? 796).

    ???family-name???: Chapter

    Hatwágner, MF, Vastag, G, Niskanen, VA & Kóczy, L 2019, Banking applications of FCM models. ???in??? Studies in Computational Intelligence. Studies in Computational Intelligence, ???book.volume??? 796, Springer Verlag, ???pages??? 61-72. https://doi.org/10.1007/978-3-030-00485-9_7
    Hatwágner MF, Vastag G, Niskanen VA, Kóczy L. Banking applications of FCM models. ???in??? Studies in Computational Intelligence. Springer Verlag. 2019. ???pages??? 61-72. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-030-00485-9_7
    Hatwágner, Miklós F. ; Vastag, Gyula ; Niskanen, Vesa A. ; Kóczy, L. / Banking applications of FCM models. Studies in Computational Intelligence. Springer Verlag, 2019. ???pages??? 61-72 (Studies in Computational Intelligence).
    @inbook{061dc2907bb743e8b412b1fc156cf69f,
    title = "Banking applications of FCM models",
    abstract = "Fuzzy Cognitive Map (FCMs) is an appropriate tool to describe, qualitatively analyze or simulate the behavior of complex systems. FCMs are bipolar fuzzy graphs: their building blocks are the concepts and the arcs. Concepts represent the most important components of the system, the weighted arcs define the strength and direction of cause-effect relationships among them. FCMs are created by experts in several cases. Despite the best intention the models may contain subjective information even if it was created by multiple experts. An inaccurate model may lead to misleading results, therefore it should be further analyzed before usage. Our method is able to automatically modify the connection weights and to test the effect of these changes. This way the hidden behavior of the model and the most influencing concepts can be mapped. Using the results the experts may modify the original model in order to achieve their goal. In this paper the internal operation of a department of a bank is modeled by FCM. The authors show how the modification of the connection weights affect the operation of the institute. This way it is easier to understand the working of the bank, and the most threatening dangers of the system getting into an unstable (chaotic or cyclic state) can be identified and timely preparations become possible.",
    keywords = "Bacterial evolutionary algorithm, Banking, Fuzzy cognitive maps, Model uncertainty, Multiobjective optimization",
    author = "Hatw{\'a}gner, {Mikl{\'o}s F.} and Gyula Vastag and Niskanen, {Vesa A.} and L. K{\'o}czy",
    year = "2019",
    month = "1",
    day = "1",
    doi = "10.1007/978-3-030-00485-9_7",
    language = "English",
    series = "Studies in Computational Intelligence",
    publisher = "Springer Verlag",
    pages = "61--72",
    booktitle = "Studies in Computational Intelligence",

    }

    TY - CHAP

    T1 - Banking applications of FCM models

    AU - Hatwágner, Miklós F.

    AU - Vastag, Gyula

    AU - Niskanen, Vesa A.

    AU - Kóczy, L.

    PY - 2019/1/1

    Y1 - 2019/1/1

    N2 - Fuzzy Cognitive Map (FCMs) is an appropriate tool to describe, qualitatively analyze or simulate the behavior of complex systems. FCMs are bipolar fuzzy graphs: their building blocks are the concepts and the arcs. Concepts represent the most important components of the system, the weighted arcs define the strength and direction of cause-effect relationships among them. FCMs are created by experts in several cases. Despite the best intention the models may contain subjective information even if it was created by multiple experts. An inaccurate model may lead to misleading results, therefore it should be further analyzed before usage. Our method is able to automatically modify the connection weights and to test the effect of these changes. This way the hidden behavior of the model and the most influencing concepts can be mapped. Using the results the experts may modify the original model in order to achieve their goal. In this paper the internal operation of a department of a bank is modeled by FCM. The authors show how the modification of the connection weights affect the operation of the institute. This way it is easier to understand the working of the bank, and the most threatening dangers of the system getting into an unstable (chaotic or cyclic state) can be identified and timely preparations become possible.

    AB - Fuzzy Cognitive Map (FCMs) is an appropriate tool to describe, qualitatively analyze or simulate the behavior of complex systems. FCMs are bipolar fuzzy graphs: their building blocks are the concepts and the arcs. Concepts represent the most important components of the system, the weighted arcs define the strength and direction of cause-effect relationships among them. FCMs are created by experts in several cases. Despite the best intention the models may contain subjective information even if it was created by multiple experts. An inaccurate model may lead to misleading results, therefore it should be further analyzed before usage. Our method is able to automatically modify the connection weights and to test the effect of these changes. This way the hidden behavior of the model and the most influencing concepts can be mapped. Using the results the experts may modify the original model in order to achieve their goal. In this paper the internal operation of a department of a bank is modeled by FCM. The authors show how the modification of the connection weights affect the operation of the institute. This way it is easier to understand the working of the bank, and the most threatening dangers of the system getting into an unstable (chaotic or cyclic state) can be identified and timely preparations become possible.

    KW - Bacterial evolutionary algorithm

    KW - Banking

    KW - Fuzzy cognitive maps

    KW - Model uncertainty

    KW - Multiobjective optimization

    UR - http://www.scopus.com/inward/record.url?scp=85054713528&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85054713528&partnerID=8YFLogxK

    U2 - 10.1007/978-3-030-00485-9_7

    DO - 10.1007/978-3-030-00485-9_7

    M3 - Chapter

    T3 - Studies in Computational Intelligence

    SP - 61

    EP - 72

    BT - Studies in Computational Intelligence

    PB - Springer Verlag

    ER -