IDEAS home Printed from https://ideas.repec.org/a/hin/complx/1303241.html
   My bibliography  Save this article

Mechanism for Measuring System Complexity Applying Sensitivity Analysis

Author

Listed:
  • Viviane M. Gomes
  • Joao R. B. Paiva
  • Marcio R. C. Reis
  • Gabriel A. Wainer
  • Wesley P. Calixto

Abstract

This work proposes a complexity metric which maps internal connections of the system and its relationship with the environment through the application of sensitivity analysis. The proposed methodology presents (i) system complexity metric, (ii) system sensitivity metric, and (iii) two models as case studies. Based on the system dynamics, the complexity metric maps the internal connections through the states of the system and the metric of sensitivity evaluates the contribution of each parameter to the output variability. The models are simulated in order to quantify the complexity and the sensitivity and to analyze the behavior of the systems leading to the assumption that the system complexity is closely linked to the most sensitive parameters. As findings from results, it may be observed that systems may exhibit high performance as a result of optimized configurations given by their natural complexity.

Suggested Citation

  • Viviane M. Gomes & Joao R. B. Paiva & Marcio R. C. Reis & Gabriel A. Wainer & Wesley P. Calixto, 2019. "Mechanism for Measuring System Complexity Applying Sensitivity Analysis," Complexity, Hindawi, vol. 2019, pages 1-12, April.
    • Handle: RePEc:hin:complx:1303241
    DOI: 10.1155/2019/1303241
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2019/1303241.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2019/1303241.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2019/1303241?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Michael Batty & Robin Morphet & Paolo Masucci & Kiril Stanilov, 2014. "Entropy, complexity, and spatial information," Journal of Geographical Systems, Springer, vol. 16(4), pages 363-385, October.
    2. Nam Ho Kim & Haoyu Wang & Nestor V. Queipo, 2006. "Adaptive reduction of random variables using global sensitivity in reliability-based optimisation," International Journal of Reliability and Safety, Inderscience Enterprises Ltd, vol. 1(1/2), pages 102-119.
    3. Sobol’, I.M. & Tarantola, S. & Gatelli, D. & Kucherenko, S.S. & Mauntz, W., 2007. "Estimating the approximation error when fixing unessential factors in global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 92(7), pages 957-960.
    4. Rosso, Osvaldo A. & Carpi, Laura C. & Saco, Patricia M. & Gómez Ravetti, Martín & Plastino, Angelo & Larrondo, Hilda A., 2012. "Causality and the entropy–complexity plane: Robustness and missing ordinal patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 42-55.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. João R. B. Paiva & Alana S. Magalhães & Pedro H. F. Moraes & Júnio S. Bulhões & Wesley P. Calixto, 2021. "Stability Metric Based on Sensitivity Analysis Applied to Electrical Repowering System," Energies, MDPI, vol. 14(22), pages 1-21, November.
    2. Amr Mahmoud & Mohamed Zohdy, 2022. "Dynamic Lyapunov Machine Learning Control of Nonlinear Magnetic Levitation System," Energies, MDPI, vol. 15(5), pages 1-16, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Borgonovo, E., 2010. "Sensitivity analysis with finite changes: An application to modified EOQ models," European Journal of Operational Research, Elsevier, vol. 200(1), pages 127-138, January.
    2. David, Viviane & Joachim, Sandrine & Tebby, Cleo & Porcher, Jean-Marc & Beaudouin, Rémy, 2019. "Modelling population dynamics in mesocosms using an individual-based model coupled to a bioenergetics model," Ecological Modelling, Elsevier, vol. 398(C), pages 55-66.
    3. Sobol' Ilya M. & Shukhman Boris V., 2007. "On Global Sensitivity Indices: Monte Carlo Estimates Affected by Random Errors," Monte Carlo Methods and Applications, De Gruyter, vol. 13(1), pages 89-97, April.
    4. Zhao, Yunjie & Cheng, Xi & Zhang, Taihong & Wang, Lei & Shao, Wei & Wiart, Joe, 2023. "A global–local attention network for uncertainty analysis of ground penetrating radar modeling," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. David Bawden & Lyn Robinson, 2015. "“Waiting for Carnot”: Information and complexity," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2177-2186, November.
    6. Stajić, Ljubiša & Praksová, Renáta & Brkić, Dejan & Praks, Pavel, 2024. "Estimation of global natural gas spot prices using big data and symbolic regression," Resources Policy, Elsevier, vol. 95(C).
    7. Beccacece, F. & Borgonovo, E., 2011. "Functional ANOVA, ultramodularity and monotonicity: Applications in multiattribute utility theory," European Journal of Operational Research, Elsevier, vol. 210(2), pages 326-335, April.
    8. Abimbola, Olufemi P. & Franz, Trenton E. & Rudnick, Daran & Heeren, Derek & Yang, Haishun & Wolf, Adam & Katimbo, Abia & Nakabuye, Hope N. & Amori, Anthony, 2022. "Improving crop modeling to better simulate maize yield variability under different irrigation managements," Agricultural Water Management, Elsevier, vol. 262(C).
    9. Sun, Xifu & Croke, Barry & Roberts, Stephen & Jakeman, Anthony, 2021. "Comparing methods of randomizing Sobol′ sequences for improving uncertainty of metrics in variance-based global sensitivity estimation," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    10. Medeiros-Sousa, Antônio Ralph & Lange, Martin & Mucci, Luis Filipe & Marrelli, Mauro Toledo & Grimm, Volker, 2024. "Modelling the transmission and spread of yellow fever in forest landscapes with different spatial configurations," Ecological Modelling, Elsevier, vol. 489(C).
    11. Montani, Fernando & Deleglise, Emilia B. & Rosso, Osvaldo A., 2014. "Efficiency characterization of a large neuronal network: A causal information approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 58-70.
    12. Mara, Thierry Alex, 2009. "Extension of the RBD-FAST method to the computation of global sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 94(8), pages 1274-1281.
    13. Damblin, Guillaume & Ghione, Alberto, 2021. "Adaptive use of replicated Latin Hypercube Designs for computing Sobol’ sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    14. Chen, Yanguang, 2020. "Equivalent relation between normalized spatial entropy and fractal dimension," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    15. Sobol’, I.M. & Kucherenko, S., 2009. "Derivative based global sensitivity measures and their link with global sensitivity indices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(10), pages 3009-3017.
    16. Deman, G. & Konakli, K. & Sudret, B. & Kerrou, J. & Perrochet, P. & Benabderrahmane, H., 2016. "Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 156-169.
    17. Zhang, Yongping & Manley, Ed & Martens, Karel & Batty, Michael, 2024. "A metro smart card data-based analysis of group travel behaviour in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 114(C).
    18. Mateos, Diego M. & Zozor, Steeve & Olivares, Felipe, 2020. "Contrasting stochasticity with chaos in a permutation Lempel–Ziv complexity — Shannon entropy plane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    19. Di Vece, Marzio & Garlaschelli, Diego & Squartini, Tiziano, 2023. "Reconciling econometrics with continuous maximum-entropy network models," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    20. Calum Brown & Evan Brown & Dave Murray-Rust & George Cojocaru & Cristina Savin & Mark Rounsevell, 2015. "Analysing uncertainties in climate change impact assessment across sectors and scenarios," Climatic Change, Springer, vol. 128(3), pages 293-306, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:1303241. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.