A value of prediction model to estimate optimal response time to threats for accident prevention

This paper presents a novel value of (imperfect) prediction (VoP) model to estimate optimal response time to a threat that may result in an accident. The proposed VoP model is based on information value theory and con-siders both prediction accuracy and action failure probability over time. The optimal response time is dependent on parameters: the ratio between the accident cost and response action cost, accident probability, action failure probability, prediction performance, and response strategy (a series of sequential responses or a single response). A case study of iceberg management is presented to demonstrate the proposed approach; a sensitivity study is done to evaluate how optimal response time changes with those parameters. The case study show that it is reasonable to respond as early as possible if the threat can lead to a serious accident, while the response can be postponed when the potential consequence is moderate. In addition, the proposed VoP model is proven able to calculate accuracy requirements, thresholds for tolerating risk and acting precautionarily, and maximum in-vestment in accident prevention. Imperfect prediction can lower risk acceptance threshold and higher the threshold of being precautionary; and it is reasonable to increase action cost.

Automated summary

This paper proposes a novel value of (imperfect) prediction (VoP) model to estimate the optimal response time to potential threats that may lead to accidents. The model considers prediction accuracy, action failure probability, and parameters such as accident cost, response action cost, accident probability, prediction performance, and response strategy. A case study on iceberg management demonstrates the effectiveness of the proposed approach, and a sensitivity study evaluates how optimal response time changes with different parameters. The study shows that responding as early as possible is reasonable for serious threats, while response can be postponed for moderate consequences. The VoP model can also calculate accuracy requirements, risk tolerance thresholds, precautionary measures, and maximum investment in accident prevention.