This study describes a critical assessment of the risk control decision model from a methodological perspective and identifies major shortcomings with the employment of enhanced formal evaluation and decision-making methods. This in turn could have major applications for natural disaster risk control. The methodology follows the description of interpretive structural modeling (ISM), which is an interactive learning process in which a set of different and directly related elements is structured to form a comprehensive systemic model. The next step explores the potentials of different mathematical programming approaches in order to improve decision making, i.e., for the development of an economic utility constrained-maximization model that addresses the issue of optimal budget allocation under a trade-off framework. Several aspects of risk and uncertainty are discussed within the context of an economic utility constrained-maximization model with a major focus on the importance of risk and uncertainty in research evaluation, and how the strategy determines the insurance and risk control plans.
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