A stratified decision-making model for long-term planning: Application in flood risk management in Scotland

Amin Vafadarnikjoo, Konstantinos Chalvatzis, Tiago Botelho, David Bamford

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12 Citations (Scopus)
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In a standard decision-making model for a game of chance, the best strategy is chosen based on the current state of the system under various conditions. There is however a shortcoming of this standard model, in that it can be applicable only for short-term decision-making periods. This is primarily due to not evaluating the dynamic characteristics and changes in status of the system and the outcomes of nature towards an a priori target or ideal state, which can occur in longer periods. Thus, in this study, a decision-making model based on the concept of stratification (CST), game theory and shared socio-economic pathway (SSP) is developed and its applicability to disaster management is shown. The game of chance and CST have been integrated to incorporate the dynamic nature of the decision environment for long-term disaster risk planning, while accounting for various states of the system and an ideal state. Furthermore, an interactive web application with dynamic user interface is built based on the proposed model to enable decision makers to identify the best choices in their model by a predictive approach. The Monte Carlo simulation is applied to experimentally validate the proposed model. Then, it is demonstrated how this methodology can suitably be applied to obtain ad hoc models, solutions, and analysis in the strategic decision-making process of flooding risk strategy evaluation. The model's applicability is shown in an uncertain real-world decision-making context, considering dynamic nature of socio-economic situations and flooding hazards in the Highland and Argyll Local Plan District in Scotland. The empirical results show that flood forecasting and awareness raising are the two most beneficial mitigation strategies in the region followed by emergency plans/response, planning policies, maintenance, and self help.
Original languageEnglish
Article number102803
Early online date13 Nov 2022
Publication statusPublished - Apr 2023

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