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Entropy 2016, 18(11), 404;

Decision-Making Model under Risk Assessment Based on Entropy

State Key Laboratory for Diasaster Prevention & Mitigation of Explosion & Impact, PLA University of Science and Technology, Nanjing 210007, China
Author to whom correspondence should be addressed.
Academic Editors: Kevin H. Knuth and Adom Giffin
Received: 23 September 2016 / Revised: 3 November 2016 / Accepted: 10 November 2016 / Published: 15 November 2016
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Decision-making under risk assessment involves dealing with the matter of uncertainty, especially in projects such as tunnel construction. Risk control should include not only measures to reduce the possible consequence of incident, but also exploration measures (information collecting measures) to reduce the uncertainty of the incident. The classical risk assessment model in engineering is R = P × C which only takes account of the assessment and decision-making of possible consequences. It cannot provide theoretical guidance for taking exploration measures. The paper presents an advanced methodology to assess the effectiveness of exploration measures in decision-making. The methodology classifies risk into two attributes: hazard (expected value) and uncertainty (entropy). On this basis, a generalized model of decision-making under risk assessment is proposed. This model extends the use of the classical assessment model to a more general case. The reason for taking exploration measures and assessment of such measures’ effectiveness could be explained well by this developed model. This model can also serve as a descriptive model for many risk problems and provide a decision-making basis for a variety of risk types. Moreover, the assessment process and calculation method are applied with some case studies. View Full-Text
Keywords: risk analysis; entropy; uncertainty; decision-making; risk assessment model risk analysis; entropy; uncertainty; decision-making; risk assessment model

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Dong, X.; Lu, H.; Xia, Y.; Xiong, Z. Decision-Making Model under Risk Assessment Based on Entropy. Entropy 2016, 18, 404.

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