Resilience is a complex system that represents dynamic behaviours through its complicated structure with various nodes, interrelations, and information flows. Like other international organizations NATO has also been dealing with the measurement of this complex phenomenon in order to have a comprehensive understanding of the civil environment and its impact on military operations. With this ultimate purpose, NATO had developed and executed a prototype model with the system dynamics modelling and simulation paradigm. NATO has created an aggregated resilience model as an upgrade of the prototype one, as discussed within this study. The structure of the model, aggregation mechanism and shock parametrization methodologies used in the development of the model comprise the scope of this study. Analytic Hierarchy Process (AHP), which is a multi-criteria decision-making technique is the methodology that is used for the development of the aggregation mechanism. The main idea of selecting the AHP methodology is its power and usefulness in mitigating bias in the decision-making process, its capability to increase the number of what-if scenarios to be created, and its contribution to the quality of causal explanations with the granularity it provides. The parametrized strategic shock input page, AHP-based weighted resilience and risk parameters input pages, one more country insertion to the model, and the decision support system page enhance the capacity of the prototype model. As part of the model, the decision support system page stands out as the strategic level cockpit where the colour codes give a clear idea at first about the overall situational picture and country-wise resilience and risk status. At the validation workshop, users not only validated the model but also discussed further development opportunities, such as adding more strategic shocks into the model and introduction of new parameters that will be determined by a big data analysis on relevant open source databases. The developed model has the potential to inspire high-level decision-makers dealing with resilience management in other international organizations, such as the United Nations.

Department of Intelligence Support University of Defence 66210 Brno Czech Republic

Department of Tactics University of Defence 66210 Brno Czech Republic

NATO Headquarters Supreme Allied Commander Transformation Norfolk VA 23511 USA

STM Savunma Teknolojileri Mühendislik ve Ticaret A S Ankara 06530 Turkey

See more in PubMed

Scherzera S., Lujalab P., Røda J.K. A Community Resilience Index for Norway: An adaptation of the Baseline Resilience Indicators for Communities (BRIC) Int. J. Disaster Risk Reduct. 2019;36:101107. doi: 10.1016/j.ijdrr.2019.101107. DOI

Hodicky J., Özkan G., Özdemir H., Stodola P., Drozd J., Buck W. Dynamic Modeling for Resilience Measurement: NATO Resilience Decision Support Model. Appl. Sci. 2020;10:2639. doi: 10.3390/app10082639. PubMed DOI PMC

ACO . Interim Direction and Guidance for Resilience through Civil Preparedness (SH/PD/BJM/0092-19) ACO; Merchtem, Belgium: 2019.

Jones L. Ph.D. Thesis. London School of Economics and Political Science; London, UK: 2020. Essays on Resilience Measurement.

Bollt. E.M., Sun J. Editorial Comment on the Special Issue of “Information in Dynamical Systems and Complex Systems”. Entropy. 2014;16:5068–5077.

Zhou Y., Tang Y., Zhao X. A Novel Uncertainty Management Approach for Air Combat Situation Assessment Based on Improved Belief Entropy. Entropy. 2019;21:495. PubMed PMC

Tchakounté F., Faissal A., Atemkeng M., Ntyam A. A Reliable Weighting Scheme for the Aggregation of Crowd Intelligence to Detect Fake News. Information. 2020;11:319. doi: 10.3390/info11060319. DOI

He D., Xu J., Chen X. Information-Theoretic-Entropy Based Weight Aggregation Method in Multiple-Attribute Group Decision-Making. Entropy. 2016;18:171. doi: 10.3390/e18060171. DOI

Balaei B., Wilkinson S., Potangaroa R., Adamson C., Alavi-Shoshtari M. Social Factors Affecting Water Supply Resilience to Disasters. Int. J. Disaster Risk Reduct. 2019;37:101187. doi: 10.1016/j.ijdrr.2019.101187. DOI

Liu Y., Yang H., Zhao Q. Hierarchical Feature Aggregation from Body Parts for Misalignment Robust Person Re-Identification. Appl. Sci. 2019;9:2255. doi: 10.3390/app9112255. DOI

National Academies of Sciences, Engineering, and Medicine . Building and Measuring Community Resilience: Actions for Communities and the Gulf Research Program. The National Academies Press; Washington, DC, USA: 2019. PubMed DOI

Koloseni D., Helldin T., Torra V. AHP-Like Matrices and Structures—Absolute and Relative Preferences. Mathematics. 2020;8:813. doi: 10.3390/math8050813. DOI

Tariq M.I., Ahmed S., Memon N.A., Tayyaba S., Ashraf M.W., Nazir M., Hussain A., Balas V.E., Balas M.M. Prioritization of Information Security Controls through Fuzzy AHP for Cloud Computing Networks and Wireless Sensor Networks. Sensors. 2020;20:1310. doi: 10.3390/s20051310. PubMed DOI PMC

Lee M.H., Cheon D.-Y., Han S.-H. An AHP Analysis on the Habitability Performance toward the Modernized Hanok in Korea. Buildings. 2019;9:177. doi: 10.3390/buildings9080177. DOI

Özcan E., Yumuşak R., Eren T. Risk Based Maintenance in the Hydroelectric Power Plants. Energies. 2019;12:1502. doi: 10.3390/en12081502. DOI

Chen N., Yao S., Wang C., Du W. A Method for Urban Flood Risk Assessment and Zoning Considering Road Environments and Terrain. Sustainability. 2019;11:2734. doi: 10.3390/su11102734. DOI

Zhang J., Chen Y. Risk Assessment of Flood Disaster Induced by Typhoon Rainstorms in Guangdong Province, China. Sustainability. 2019;11:2738. doi: 10.3390/su11102738. DOI

Domínguez I., Oviedo-Ocaña E., Hurtado K., Barón A., Hall R. Assessing Sustainability in Rural Water Supply Systems in Developing Countries Using a Novel Tool Based on Multi-Criteria Analysis. Sustainability. 2019;11:5363. doi: 10.3390/su11195363. DOI

Boros A., Fogarassy C. Relationship between Corporate Sustainability and Compliance with State-Owned Enterprises in Central-Europe: A Case Study from Hungary. Sustainability. 2019;11:5653. doi: 10.3390/su11205653. DOI

Beiragh R.G., Alizadeh R., Shafiei Kaleibari S., Cavallaro F., Zolfani S.H., Bausys R., Mardani A. An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies. Sustainability. 2020;12:789. doi: 10.3390/su12030789. DOI

Ghorui A., Ghosh E., Algehyne A., Mondal S.P., Saha A.K. AHP-TOPSIS Inspired Shopping Mall Site Selection Problem with Fuzzy Data. Mathematics. 2020;8:1380. doi: 10.3390/math8081380. DOI

Xu J., Yu L., Gupta R. Evaluating the Performance of the Government Venture Capital Guiding Fund Using the Intuitionistic Fuzzy Analytic Hierarchy Process. Sustainability. 2020;12:6908. doi: 10.3390/su12176908. DOI

Wu Z., Abdul-Nour G. Comparison of Multi-Criteria Group Decision-Making Methods for Urban Sewer Network Making Methods for Urban Sewer Network Plan Selection. CivilEng. 2020;1:26–48. doi: 10.3390/civileng1010003. DOI

Song Y., Chen Z., Zhang S., Wang J., Li C., Li X., Yuan J., Zhang X. Comprehensive Evaluation System of Occupational Hazard Prevention and Control in Iron and Steel Enterprises Based on A Modified Delphi Technique. Int. J. Environ. Res. Public Health. 2020;17:667. doi: 10.3390/ijerph17020667. PubMed DOI PMC

Forrester J.W. System Dynamics, Systems Thinking, and Soft OR. Syst. Dyn. Rev. 1994;10:245–256. doi: 10.1002/sdr.4260100211. DOI

Sterman J.D. Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin/McGraw-Hill; Boston, MA, USA: 2000.

Saaty T.L. Decision Making with the Analytic Hierarchy Process. Int. J. Serv. Sci. 2008;1:83. doi: 10.1504/IJSSCI.2008.017590. DOI

Saaty T.L. How to Make a Decision: The Analytic Hierarchy Process. Eur. J. Oper. Res. 1990;48:9–26. doi: 10.1016/0377-2217(90)90057-I. PubMed DOI

Gottwald S., Braun D.A. Bounded Rational Decision-Making from Elementary Computations That Reduce Uncertainty. Entropy. 2019;21:375. doi: 10.3390/e21040375. PubMed DOI PMC

Artificial Intelligence and Computational Methods in the Modeling of Complex Systems

Analytic Hierarchy Process (AHP)-Based Aggregation Mechanism for Resilience Measurement: NATO Aggregated Resilience Decision Support Model