@article{Duffey_Saull_2009, author = {Duffey, Romney and Saull, John}, title = {Managing and Predicting Maritime and Off-shore Risk}, journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation}, volume = {3}, number = {2}, pages = {181-188}, year = {2009}, url = {./Article_Managing_and_Predicting_Maritime_Duffey,10,153.html}, abstract = {We wish to predict when an accident or tragedy will occur, and reduce the probability of its occurrence. Maritime accidents, just like all the other crashes and failures, are stochastic in their occurrence. They can seemingly occur as observed outcomes at any instant, without warning. They are due to a combination of human and technological system failures, working together in totally unexpected and/or undetected ways, occurring at some random moment. Massive show the cause is due to an unexpected combination or sequence of human, management, operational, design and training mistakes. Once we know what happened, we can fix the engineering or design failures, and try to obviate the human ones. We utilize reliability theory applied to humans, and show how the events rates and probability in shipping is related to other industries and events through the human involvement. We examine and apply the learning hypothesis to shipping losses and other events at sea, including example Case Studies stretching over some 200 years of: (a) merchant and fishing vessels; (b) oil spills and injuries in off-shore facilities; and (c) insurance claims, inspection rules and premiums. These include major losses and sinkings as well as the more everyday events and injuries. By using good practices and achieving a true learning environment, we can effectively defer the chance of an accident, but not indefinitely. Moreover, by watching our experience and monitoring our rate, understand and predict when we are climbing up the curve. Comparisons of the theory to all available human error data show a reasonable level of accord with the learning hypothesis. The results clearly demonstrate that the loss (human error) probability is dynamic, and may be predicted using the learning hypothesis. The future probability estimate is derivable from its unchanged prior value, based on learning, and thus the past frequency predicts the future probability. The implications for maritime activities is discussed and related to the latest work on managing risk, and the analysis of trends and safety indicators.}, issn = {2083-6473}, publisher = {Gdynia Maritime University, Faculty of Navigation}, keywords = {Maritime Risk, Offshore Risk, Marine Accidents, Universal Learning Curve (ULC), Risk Assessment, Human Failure, Shipping Losses, Managing Risk} }