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Exploratory modelling and analysis to support decision-making under deep uncertainty: A case study from defence resource planning and asset management

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  • Kahagalage, Sanath Darshana
  • Turan, Hasan Hüseyin
  • Elsawah, Sondoss
  • Gary, Michael Shayne

Abstract

Emerging approaches and tools in the literature for model-based decision support under deep uncertainty are rapidly growing. Exploratory modelling and analysis is one of the unifying ideas behind these approaches. This research leverages exploratory modelling and analysis in the context of a case study within defense resource planning for the Royal Australian Navy. We use a multi-method approach that combines exploratory modelling and analysis and a simulation model. Our research analyses a strategy from a deterministic optimization problem to assess the impact of uncertainties and to identify different model behaviours exhibited under uncertainties and the uncertainty space in which the strategy maintains reasonable performance. To achieve these objectives, we draw upon concepts from Robust Decision-Making, Behaviour-Based Scenario Discovery, and Feasible Scenario Space. The results are then analysed to derive decision-making insights and highlight the potential consequences of neglecting uncertainties. For the first time in defense resource planning, we compare the results from these three approaches and tools. These approaches and tools complement each other, leading to similar conclusions with unique insights. The results also reveal the misleading nature of considering the best approximate conditions in the predictive use of simulation models under deep uncertainty.

Suggested Citation

  • Kahagalage, Sanath Darshana & Turan, Hasan Hüseyin & Elsawah, Sondoss & Gary, Michael Shayne, 2024. "Exploratory modelling and analysis to support decision-making under deep uncertainty: A case study from defence resource planning and asset management," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:tefoso:v:200:y:2024:i:c:s0040162523008351
    DOI: 10.1016/j.techfore.2023.123150
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    References listed on IDEAS

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