IDEAS home Printed from https://ideas.repec.org/a/wly/syseng/v21y2018i2p105-114.html

Risk modeling of communications, navigation, and surveillance complex systems of systems for future aviation

Author

Listed:
  • Bryan R. Lewis
  • Yacov Y. Haimes

Abstract

The planned integration of Communications (C), Navigation (N), and Surveillance (S) by the U.S. Federal Aviation Administration (FAA) would convert their currently safely coordinated operations into a single vulnerable interdependent and interconnected CNS complex systems of systems (Complex SoS). We model the ensuing vulnerability of planned Complex SoS by tracing specific new interdependencies and interconnections manifested by their shared/common (i) States (variables), (ii) Decisions, (iii) Decision makers, and (iv) Resources (cyber‐physical) among the three C, N, and S systems?. Other contributions stem from our exploiting the use of fault trees (without the reliance on reliability data). Exploiting fault trees enabled us to quantify the accrued knowledge of interdependencies and interconnectedness tabulated in Tables 1–4 in terms of AND Gates (systems connected in Parallel), and OR Gates (systems connected in Series); and to discover dangerous shared C, N, and S systems on the critical path. Namely, if all systems (subsystems) in the minimal cut set fail, then the entire CNS Complex SoS would fail. Significantly, this modeling approach alerted the FAA on “what not to do” by identifying subsystems early in the planned integration of C, N, and S that would result in a single catastrophic failure of the planned CNS Complex SoS.

Suggested Citation

  • Bryan R. Lewis & Yacov Y. Haimes, 2018. "Risk modeling of communications, navigation, and surveillance complex systems of systems for future aviation," Systems Engineering, John Wiley & Sons, vol. 21(2), pages 105-114, March.
    • Handle: RePEc:wly:syseng:v:21:y:2018:i:2:p:105-114
    DOI: 10.1002/sys.21423
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/sys.21423
    Download Restriction: no
    File URL: https://libkey.io/10.1002/sys.21423?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. Stanley Kaplan & B. John Garrick, 1981. "On The Quantitative Definition of Risk," Risk Analysis, John Wiley & Sons, vol. 1(1), pages 11-27, March.
    2. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    Full references (including those not matched with items on IDEAS)

    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. Yacov Y. Haimes, 2018. "Risk Modeling of Interdependent Complex Systems of Systems: Theory and Practice," Risk Analysis, John Wiley & Sons, vol. 38(1), pages 84-98, January.
    2. Popoyan, Lilit & Napoletano, Mauro & Roventini, Andrea, 2017. "Taming macroeconomic instability: Monetary and macro-prudential policy interactions in an agent-based model," Journal of Economic Behavior & Organization, Elsevier, vol. 134(C), pages 117-140.
    3. Kubin, Ingrid & Zörner, Thomas O. & Gardini, Laura & Commendatore, Pasquale, 2019. "A credit cycle model with market sentiments," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 159-174.
    4. Klaus Jaffe, 2015. "Agent based simulations visualize Adam Smith's invisible hand by solving Friedrich Hayek's Economic Calculus," Papers 1509.04264, arXiv.org, revised Nov 2015.
    5. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    6. Zhang, Hui & Cao, Libin & Zhang, Bing, 2017. "Emissions trading and technology adoption: An adaptive agent-based analysis of thermal power plants in China," Resources, Conservation & Recycling, Elsevier, vol. 121(C), pages 23-32.
    7. Liu, Beibei & He, Pan & Zhang, Bing & Bi, Jun, 2012. "Impacts of alternative allowance allocation methods under a cap-and-trade program in power sector," Energy Policy, Elsevier, vol. 47(C), pages 405-415.
    8. Rich, Karl M. & Ross, R. Brent & Baker, A. Derek & Negassa, Asfaw, 2011. "Quantifying value chain analysis in the context of livestock systems in developing countries," Food Policy, Elsevier, vol. 36(2), pages 214-222, April.
    9. V. Alfi & L. Pietronero & A. Zaccaria, 2008. "Minimal Agent Based Model For The Origin And Self-Organization Of Stylized Facts In Financial Markets," Papers 0807.1888, arXiv.org.
    10. Matteo Coronese & Davide Luzzati, 2022. "Economic impacts of natural hazards and complexity science: a critical review," LEM Papers Series 2022/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    11. Ozgur Satici & Esra Satici, 2024. "Theoretical semi-quantitative risk assessment methodology for tunnel design and construction processes," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(7), pages 3385-3405, July.
    12. Cees Diks & Cars Hommes & Valentyn Panchenko & Roy Weide, 2008. "E&F Chaos: A User Friendly Software Package for Nonlinear Economic Dynamics," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 221-244, September.
    13. Chen, Fuzhong & Hsu, Chien-Lung & Lin, Arthur J. & Li, Haifeng, 2020. "Holding risky financial assets and subjective wellbeing: Empirical evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    14. Qingxu Huang & Dawn C Parker & Tatiana Filatova & Shipeng Sun, 2014. "A Review of Urban Residential Choice Models Using Agent-Based Modeling," Environment and Planning B, , vol. 41(4), pages 661-689, August.
    15. Serena Brianzoni & Roy Cerqueti & Elisabetta Michetti, 2010. "A Dynamic Stochastic Model of Asset Pricing with Heterogeneous Beliefs," Computational Economics, Springer;Society for Computational Economics, vol. 35(2), pages 165-188, February.
    16. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
    17. Ashraf, Quamrul & Gershman, Boris & Howitt, Peter, 2017. "Banks, market organization, and macroeconomic performance: An agent-based computational analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 143-180.
    18. Luca Riccetti & Alberto Russo & Mauro Gallegati, 2015. "An agent based decentralized matching macroeconomic model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(2), pages 305-332, October.
    19. Niël Almero Krüger & Natanya Meyer, 2021. "The Development of a Small and Medium-Sized Business Risk Management Intervention Tool," JRFM, MDPI, vol. 14(7), pages 1-14, July.
    20. Sjur Didrik Flåm, 2013. "Reaching Market Equilibrium Merely by Bilateral Barters," CESifo Working Paper Series 4504, CESifo.

    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:wly:syseng:v:21:y:2018:i:2:p:105-114. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1520-6858 .

    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.