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Contents <p/> <b>About the Authors</b> <p/> <b>Preface</b> <p/> <b>Acknowledgements</b> <p/> <b>Defi nitions of Risk and Risk Management</b> <p/> <b>Introduction: The Art of Prediction and the Creation of Order</b> <p/> Risk and Risk Management <p/> Defi ning Risk <p/> Managing Risk: Our Purpose, Plan and Goals <p/> Recent Tragic Outcomes <p/> Power Blackouts, Space Shuttle Losses, Concorde Crashes, <st1:City w:st="on">Chernobyl</st1:City>, <st1:place w:st="on">Three Mile Island</st1:place> and More . . . <p/> How Events and Disasters Evolve in a Phased Development: The Human Element <p/> Our Values at Risk: The Probable Improvement <p/> Probably or Improbably Not <p/> How this Book is Organised <p/> References <p/> <b>Technical Summary</b> <p/> Defi ning the Past Probability <p/> Predicting Future Risk: Sampling from the Jar of Life <p/> A Possible Future: Defi ning the Posterior Probability <p/> The Engineers Have an Answer: Reliability <p/> Drawing from the Jar of Life: The Hazard Function and Species Extinction <p/> Experiencing Failure: Engineering and Human Risk and Reliability <p/> Experience Space <p/> Managing Safely: Creating Order out of Disorder Using Safety Management Systems <p/> Describing the Indescribable: Top-Down and Bottom-Up <p/> What an Observer will Observe and the Depth of our Experience <p/> References <p/> <b>1 The Universal Learning Curve</b> <p/> Predicting Tragedies, Accidents and Failures: Using the Learning Hypothesis <p/> The Learning Hypothesis: The Market Place of Life <p/> Learning in HTSs: The Way a Human Learns <p/> Evidence of Risk Reduction by Learning <p/> Evidence of Learning from Experience: Case Studies <p/> Evidence of Learning in Economics <p/> Evidence of Learning in Engineering and Architecture: The Costs of Mistakes <p/> Learning in Technology: the Economics of Reducing Costs <p/> Evidence of Learning Skill and Risk Reduction in the Medical Profession: Practice Makes Almost Perfect <p/> Learning in HTSs: The Recent Data Still Agrees <p/> The Equations That Describe the Learning Curve <p/> Zero Defects and Reality <p/> Predicting Failures: The Human Bathtub <p/> Experience Space: The Statistics of Managing Safety and of Observing Accidents <p/> Predicting the Future Based on Past Experience: The Prior Ignorance <p/> Future Events: the Way Forward Using Learning Probabilities <p/> The Wisdom of Experience and Inevitability <p/> The Last, First or Rare Event <p/> Conclusions and Observations: Predicting Accidents <p/> References <p/> <b>2 The Four Echoes</b> <p/> Power Blackouts, Space Shuttle Losses, Concorde Crashes, and the <st1:City w:st="on">Chernobyl</st1:City> and <st1:place w:st="on">Three Mile Island</st1:place> Accidents <p/> The Combination of Events <p/> The Problem Is the Human Element <p/> The Four Echoes Share the Same Four Phases <p/> The First Echo: Blackout of the Power Grid <p/> Management’s Role <p/> The First Echo: Findings <p/> <st1:place w:st="on"><st1:PlaceName w:st="on">Error</st1:PlaceName> <st1:PlaceType w:st="on">State</st1:PlaceType></st1:place> Elimination <p/> The Second Echo: Columbia/Challenger <p/> The Results of the Inquiry: Prior Knowledge <p/> The Second Echo: The Four Phases <p/> Management’s Responsibility <p/> <st1:place w:st="on"><st1:PlaceName w:st="on">Error</st1:PlaceName> <st1:PlaceType w:st="on">State</st1:PlaceType></st1:place> Elimination <p/> The Third Echo: Concorde Tires and SUVs <p/> Tire Failures: the Prior Knowledge <p/> The Third Echo: The Four Phases <p/> Management’s Responsibility <p/> <st1:place w:st="on"><st1:PlaceName w:st="on">Error</st1:PlaceName> <st1:PlaceType w:st="on">State</st1:PlaceType></st1:place> Elimination <p/> The Fourth Echo: <st1:City w:st="on"><st1:place w:st="on">Chernobyl</st1:place></st1:City> <p/> The <st1:City w:st="on">Chernobyl</st1:City> Accident: An Echo of <st1:place w:st="on">Three Mile Island</st1:place> <p/> The Consequences <p/> Echoes of <st1:place w:st="on">Three Mile Island</st1:place> <p/> The Causes <p/> <st1:place w:st="on"><st1:PlaceName w:st="on">Error</st1:PlaceName> <st1:PlaceType w:st="on">State</st1:PlaceType></st1:place> Elimination <p/> The Fourth Echo: The Four Phases <p/> Regulatory Environment and Practices <p/> Case study: Regulation in Commercial Aviation <p/> <i>a) Regulations Development</i> <p/> <i>b) Compliance Standards</i> <p/> <i>c) Accident Investigation</i> <p/> Addressing Human Error <p/> Management Responsibilities <p/> Designing to Reduce Risk and the Role of Standards <p/> Conclusion and Echoes: Predicting the Unpredictable <p/> References <p/> <b>3 Predicting Rocket Risks and Refi nery Explosions: Near Misses, Shuttles, Safety and Anti-Missile Defence Systems Effectiveness</b> <p/> Learning from Near Misses and Prior Knowledge <p/> Problems in Quantifying Risk: Predicting the Risk for the Next Shuttle <st1:City w:st="on"><st1:place w:st="on">Mission</st1:place></st1:City> <p/> Estimating a Possible <st1:place w:st="on"><st1:PlaceType w:st="on">Range</st1:PlaceType> of <st1:PlaceName w:st="on">Likelihoods</st1:PlaceName></st1:place> <p/> Learning from Experience: Maturity Models for Future Space <st1:City w:st="on"><st1:place w:st="on">Mission</st1:place></st1:City> Risk <p/> Technology versus Technology <p/> Missiles Risks over <st1:City w:st="on"><st1:place w:st="on">London</st1:place></st1:City>: The German Doodlebug <p/> Launching Missile Risk <p/> The Number of Tests Required <p/> Estimating the Risk of a Successful Attack and How Many Missiles We Must Fire <p/> Uncertainty in the Risk of Failing to Intercept <p/> What Risk Is There of a Missile Getting Through: Missing the Missile <p/> Predicting the Risk of Industrial Accidents: The <st1:City w:st="on"><st1:place w:st="on">Texas City</st1:place></st1:City> Refinery Explosion <p/> From Lagging to Leading: Safety Analysis and Safety Culture <p/> Missing Near Misses <p/> What these Risk Estimates Tell Us: The Common Sense Echo <p/> References <p/> <b>4 The Probability of Human Error: Learning in Technological Systems</b> <p/> What We Must Predict <p/> The Probability Linked to the Rate of Errors <p/> The Defi nition of Risk Exposure and the Level of Attainable Perfection <p/> Comparison to Conventional Social Science and Engineering Failure and Outcome Rate Formulations <p/> The Learning Probabilities and the PDFs <p/> The Initial Failure Rate and its Variation with Experience <p/> The ‘Best’ MERE Risk Values <p/> Maximum and Minimum Likely Outcome Rates <p/> Standard Engineering Reliability Models Compared to the MERE Result <p/> Future Event Estimates: The Past Predicts the Future <p/> Statistical Bayesian-Type Estimates: The Impact of Learning <p/> Maximum and Minimum Likelihood <p/> Comparison to Data: The Probability of Failure and Human Error <p/> Comparison of the MERE Result to Human Reliability Analysis <p/> Implications for Generalised Risk Prediction <p/> Conclusions: The Probable Human Risk <p/> References <p/> <b>5 Eliminating Mistakes: The Concept of Error States</b> <p/> A General Accident Theory: <st1:place w:st="on"><st1:PlaceName w:st="on">Error</st1:PlaceName> <st1:PlaceType w:st="on">States</st1:PlaceType></st1:place> and Safety Management <p/> The Physics of Errors <p/> The Learning Hypothesis and the General Accident Theory <p/> Observing Outcomes <p/> A Homage to Boltzmann: Information from the Grave <p/> The Concept of Depth of Experience and the Theory of Error States <p/> The Fundamental Postulates of <st1:place w:st="on"><st1:PlaceName w:st="on">Error</st1:PlaceName> <st1:PlaceType w:st="on">State</st1:PlaceType></st1:place> Theory <p/> The Information in Error States: Establishing the Risk Distribution <p/> The Exponential Distribution of Outcomes, Risk and <st1:place w:st="on"><st1:PlaceName w:st="on">Error</st1:PlaceName> <st1:PlaceType w:st="on">States</st1:PlaceType></st1:place> <p/> The Total Number of Outcomes <p/> The Observed Rate and the Minimum Number of Outcomes <p/> Accumulated Experience Measures and Learning Rates <p/> The Average Rate <p/> Analogy and Predictions: Statistical Error Theory and Learning Model Equivalence <p/> The Infl uence of Safety Management and Regulations: Imposing Order on Disorder <p/> The Risk of Losing a Ship <p/> Distribution Functions <p/> The Most Probable and Minimum Error Rate <p/> Learning Rates and Experience Intervals: The Universal Learning Curve <p/> Reducing the Risk of a Fatal Aircraft Accident: the Infl uence of Skill and Experience <p/> Conclusions: A New Approach <p/> References <p/> <b>6 Risk Assessment: Dynamic Events and Financial Risks</b> <p/> Future Loss Rate Prediction: Ships and Tsunamis <p/> Predicted Insurance Rates for Shipping Losses: Historical Losses <p/> The Premium Equations <p/> Financial Risk: Dynamic Loss and Premium Investments <p/> Numerical Example <p/> Overall Estimates of Shipping Loss Fraction and Insurance Inspections <p/> The Loss Ratio: Deriving the Industrial Damage Curves <p/> Making Investment Decisions: Information Drawing from the Jar of Life <p/> Information Entropy and Minimum Risk <p/> Progress and Learning in Manufacturing <p/> Innovation in Technology for the Least Product Price and Cost: Reductions During Technological Learning <p/> Cost Reduction in Manufacturing and Production: Empirical Elasticity ‘Power Laws’ and Learning Rates <p/> A New General Formulation for Unit Cost Reduction in Competitive Markets: the Minimum Cost According to a Black-Scholes Formulation <p/> Universal Learning Curve: Comparison to the Usual Economic Power Laws <p/> The Learning Rate <i>b</i>-Value ‘Elasticity’ Exponent Evaluated <p/> Equivalent Average Total Cost <i>b</i>-Value Elasticity <p/> Profi t Optimisation to Exceed Development Cost <p/> The Data Validate the Learning Theory <p/> <i>a) Aircraft Manufacturing Costs Estimate Case</i> <p/> <i>b) Photovoltaic Case</i> <p/> <i>c) Air Conditioners Case</i> <p/> <i>d) Ethanol Prices Case</i> <p/> <i>e) Windpower Case</i> <p/> <i>f) Gas Turbine Power Case</i> <p/> <i>g) The Progress Curve for Manufacturing</i> <p/> Non-Dimensional UPC and Market Share <p/> Conclusions: Learning to Improve and Turning Risks into Profits <p/> References <p/> <b>7 Safety and Risk Management Systems: the Fifth Echoes</b> <p/> Safety Management Systems: Creating Order Out of Disorder <p/> Workplace Safety: The Four Rights, Four Wrongs and Four Musts <p/> Acceptable Risk: Designing for Failure and Managing for Success <p/> Managing and Risk Matrices <p/> Organisational Factors and Learning <p/> A Practical ‘Safety Culture’ Example: The Fifth Echo <p/> Safety Culture and Safety Surveys: The Learning Paradox <p/> Never Happening Again: Perfect Learning <p/> Half a World Apart: Copying the Same Factors <p/> Using a Bucket: Errors in Mixing at the JCO Plant <p/> Using a Bucket: Errors in Mixing at the <st1:place w:st="on"><st1:PlaceName w:st="on">Kean</st1:PlaceName> <st1:PlaceType w:st="on">Canyon</st1:PlaceType></st1:place> Explosives Plant <p/> The Prediction and Management of Major Hazards: Learning from SMS Failures <p/> Learning Environments and Safety Cultures: The Desiderata of Desires <p/> Safety Performance Measures: Indicators and Balanced Scorecards <p/> Safety and Performance Indicators: Measuring the Good <p/> Human Error Rates Passing Red Lights, Runway Incursions and Near Misses <p/> Risk Informed Regulation and Degrees of Goodness: How Green is Green? <p/> Modelling and Predicting Event Rates and Learning Curves Using Accumulated Experience <p/> Using the Past to Predict the Future: How Good is Good? <p/> Reportable Events <p/> Scrams and Unplanned Shutdowns <p/> Common Cause Events and Latent Errors <p/> Performance Improvement: Case-by-Case <p/> Lack of Risk Reduction: Medical Adverse Events and Deaths <p/> New Data: Sentinel Events, Deaths and Blood Work <p/> Medication Errors in Health Care <p/> Organisational Learning and Safety Culture: the ‘<i>H</i>-Factor’ <p/> Risk Indicator Data Analysis: A Case Study <p/> Meeting the Need to <i>Measure</i> Safety Culture: the Hard and the Soft Elements <p/> Creating Order from Disorder <p/> References <p/> <b>8 Risk Perception: Searching for the Truth Among all the Numbers</b> <p/> Perceptions and Predicting the Future: Risk Acceptance and Risk Avoidance <p/> Fear of the Unknown: The Success Journey into What We Do or Do Not Accept <p/> A Possible Explanation of Risk Perception: Comparisons of Road and Rail Transport <p/> How Do We Judge the Risk? <p/> Linking Complexity, Order, Information Entropy and Human Actions <p/> Response Times, Learning Data and the Universal Laws of Practice <p/> The Number and Distribution of Outcomes: Comparison to Data <p/> Risk Perception: Railways <p/> Risk Perception: Coal Mining <p/> Risk Perception: Nuclear Power in <st1:country-region w:st="on"><st1:place w:st="on">Japan</st1:place></st1:country-region> <p/> Risk Perception: Rare Events and Risk Rankings <p/> Predicting the Future Number of Outcomes <p/> A Worked Example: Searching out and Analysing Data for Oil Spills <p/> Typical Worksheet <p/> Plotting the Data <p/> Fitting a Learning Curve <p/> Challenging Zero Defects <p/> Comparison of Oil Spills to other Industries <p/> Predicting the Future: the Probability and Number of Spills <p/> Observations on this Oil Spill Case <p/> Knowing What We Do Not Know: Fear and Managing the Risk of the Unknown <p/> White and Black Paradoxes: Known Knowns and Unknown Unknowns <p/> The Probability of the Unknowns: Learning from What We Know <p/> The Existence of the Unknown: Failures in High Reliability Systems <p/> The Power of Experience: Facing Down the Fear of the Unknown <p/> Terrorism, Disasters and Pandemics: Real, Acceptable and Imaginary Risks <p/> Estimating Personal Risk of Death: Pandemics and Infectious Diseases <p/> Sabotage: Vulnerabilities, Critical Systems and the Reliability of Security Systems <p/> What Is the Risk? <p/> The Four Quadrants: Implications of Risk for Safety Management Systems <p/> References <p/> <b>9 I Must Be Learning</b> <p/> Where We Have Come From <p/> What We Have Learned <p/> What We Have Shown <p/> Legal, Professional and Corporate Implications for the Individual <p/> Just Give Me the Facts <p/> Where We Are Going <p/> Reference <p/> <b>Nomenclature</b> <p/> <b>Appendices:</b> <p/> Appendix A: The ‘Human Bathtub’: Predicting the Future Risk <p/> The Differential Formulation for the Number of Outcomes <p/> The Future Probability <p/> Insuffi cient Learning <p/> Appendix B: The Most Risk, or Maximum Likelihood, for the Outcome (Failure or Error) Rate while Learning <p/> The Most or Least Likely Outcome Rate <p/> The Maximum and Minimum Risk: The Two Solutions <p/> Low Rates and Rare Events <p/> The Limits of Maximum and Minimum Risk: The Two Solutions <p/> Common Sense: The Most Risk at the Least Experience and the Least Risk as the First Outcome Decreases with Experience <p/> Typical Trends in Our Most Likely Risk <p/> The Distribution with Depth of Experience <p/> References <p/> Appendix C: Transcripts of the Four Echoes <p/> Power Blackout, Columbia Space Shuttle loss, Concorde Crash and Chernobyl Accident <p/> The Combination of Events <p/> The Four Echoes Share the Same Four Phases <p/> Appendix. Blackout Chronology and the Dialog from Midday 14 August 2003 <p/> The Second Echo: Columbia/Challenger <p/> Appendix: Shuttle Dialog and Transcripts <p/> The Third Echo: Concorde Tires and SUVs <p/> Appendix: Dialog for the Concorde Crash <p/> The Fourth Echo: TMI/Chernobyl <p/> Appendix: Chronology and Transcripts of the <st1:City w:st="on"><st1:place w:st="on">Chernobyl</st1:place></st1:City> Reactor Unit 4 Accident <p/> Conclusion and Echoes: Predicting the Unpredictable <p/> Appendix D: The Four Phases: Fuel Leak Leading to Gliding a Jet in to Land without any Engine Power <p/> The Bare Facts and the Sequence <p/> The Four Phases <p/> Flight Crew Actions <p/> <i>Initial Recognition of the Fuel Loss</i> <p/> <i>Crew Reaction to the Fuel Imbalance Advisory (05:33–05:45)</i> <p/> <i>Crew Reaction to the Continued Fuel Loss (05:45–06:10)</i> <p/> <i>Crew Reaction to the (Two) Engine Failures</i> <p/> References <p/> Appendix E: The Four Phases of a Midair Collision <p/> The Bare Facts <p/> The Four Phases <p/> References <p/> Appendix F: Risk From the Number of Outcomes We Observe: How Many Are There? <p/> The Number of Outcomes: The Hypergeometric Distribution <p/> Few Outcomes and many Non-Outcomes: The Binomial and Poisson Distributions <p/> The Number of Outcomes: In the Limit <p/> The Perfect Learning Limit: Learning from Non-Outcomes <p/> The Relative Change in Risk When Operating Multiple Sites <p/> References <p/> Appendix G: Mixing in a Tank: The D.D. Williamson Vessel Explosion <p/> Errors in Mixing in a Tank at the Caramel Factory: The Facts <p/> The Prior Knowledge <p/> Another Echo <p/> References <p/> Appendix H: Never Happening Again <p/> The Risk of an Echo, or of a Repeat Event <p/> The Matching Probability for an Echo <p/> The Impact of Learning and Experience on Managing the Risk of Repeat Events <p/> The Theory of Evidence: Belief and Risk Equivalence <p/> References <p/> Appendix I: A Heuristic Organisational Risk Stability Criterion <p/> Order and Disorder in Physical and Management Systems <p/> Stability Criterion <p/> References <p/> Appendix J: New Laws of Practice for Learning and Error Correction <p/> Individual Learning and Practice <p/> Comparison to Error Reduction Data <p/> Comparison to Response Time Data and the Consistent Law of Practice <p/> Reconciling the Laws <p/> Conclusions <p/> References <p/> Appendix K: Predicting Rocket Launch Reliability – Case Study <p/> Summary <p/> Theory of Rocket Reliability <p/> <i>a) Unknown Total Number of Launches and Failures</i> <p/> <i>b) Known Total Number of Launches and Failures</i> <p/> Results <p/> Measures of Experience <p/> Comparsion to World Data <p/> Predicting the Probability of Failure <p/> Statistical Estimates of the Failure Probability for the Very ‘next’ launch <p/> Independent Validation of the MERE Launch Failure Curve <p/> Observations <p/> References <p/> <b>Illustrations</b> <p/> Pipeline Spill and Fire <p/> Train Crash Due to SPAD <p/> Space Shuttle <st1:City w:st="on"><st1:place w:st="on">Columbia</st1:place></st1:City> <p/> Chemical Explosion <p/> Bayes, Laplace and Bernouli <p/> <st1:place w:st="on"><st1:PlaceName w:st="on">Kean</st1:PlaceName> <st1:PlaceType w:st="on">Canyon</st1:PlaceType></st1:place> Explosion <p/> Boltzmann’s Grave <p/> <st1:State w:st="on"><st1:place w:st="on">Quebec</st1:place></st1:State> Overpass <p/> <b>Index</b>

"An excellently produced book with over 500 pages of detailed information on the management of risk and the avoidance of accidents." (<i>AMEC</i>, November 2008)

<p><strong>Romney B. Duffey, Principal Scientist, Research and Product Development, Chalk River, Atomic Energy of Canada Ltd.</strong><br />Romney B. Duffey is a leading expert in commercial nuclear reactors and is active in global environmental and energy studies and in advanced system design. He is currently Principal Scientist for AECL (Canada), having previously held a number of leadership roles within the US utility industry and in government laboratories and programs. He is a past chair of the American Society of Engineers' Nuclear Engineering Division, and the American Nuclear Society's Thermal Hydraulics Division. He has authored over 200 papers and articles. <p><strong>John W. Saull, Executive Director, International Federation of Airworthiness, UK.</strong><br />John W. Saull is an internationally renowned aeronautical engineer with over 45 years' experience in commercial aircraft certification, manufacturing, maintenance, personnel licensing and flight operations, and is a leading expert in safety management and human error. He is currently Executive Director of the International Federation of Airworthiness, having retired from his position as Chief Surveyor and Head of Operating Standards at the Civil Aviation Authority in 1996. He is currently a member of a number of international safety committees dealing with maintenance and human factors, and continues to be involved in organizing air safety conferences and chairing technical sessions.

The human element is the principle cause of incidents and accidents in all technology industries; hence it is evident that an understanding of the interaction between humans and technology is crucial to the effective management of risk. Despite this, no tested model that explicitly and quantitatively includes the human element in risk prediction is currently available. <p><i>Managing Risk: the Human Element</i> combines descriptive and explanatory text with theoretical and mathematical analysis, offering important new concepts that can be used to improve the management of risk, trend analysis and prediction, and hence affect the accident rate in technological industries. It uses examples of major accidents to identify common causal factors, or “echoes”, and argues that the use of specific experience parameters for each particular industry is vital to achieving a minimum error rate as defined by mathematical prediction. New ideas for the perception, calculation and prediction of risk are introduced, and safety management is covered in depth, including for rare events and “unknown” outcomes</p> <ul> <li>Discusses applications to multiple industries including nuclear, aviation, medical, shipping, chemical, industrial, railway, offshore oil and gas;</li> <li>Shows consistency between learning for large systems and technologies with the psychological models of learning from error correction at the personal level;</li> <li>Offers the expertise of key leading industry figures involved in safety work in the civil aviation and nuclear engineering industries;</li> <li>Incorporates numerous fascinating case studies of key technological accidents.</li> </ul> <p><i>Managing Risk: the Human Element</i> is an essential read for professional safety experts, human reliability experts and engineers in all technological industries, as well as risk analysts, corporate managers and statistical analysts. It is also of interest to professors, researchers and postgraduate students of reliability and safety engineering, and to experts in human performance.</p> <p>“<i>…congratulations on what appears to be, at a high level of review, a significant contribution to the literature…I have found much to be admired in (your) research</i>”</p> <p>Mr. Joseph Fragola – Vice President of Valador Inc.</p> <p>“<i>The book is not only technically informative, but also attractive to all concerned readers and easy to be comprehended at various level of educational background. It is truly an excellent book ever written for the safety risk managers and analysis professionals in the engineering community, especially in the high reliability organizations…”</i></p> <p>Dr Feng Hsu, Head of Risk Assessment and Management, NASA Goddard Space Flight Center</p> <p>“<i>I admire your courage in confronting your theoretical ideas with such diverse, ecologically valid data, and your success in capturing a major trend in them….I should add that I find all this quite inspiring . …The idea that you need to find the right measure of accumulated experience and not just routinely used calendar time makes so much sense that it comes as a shock to realize that this is a new idea</i>”,</p> <p>Professor Stellan Ohlsson, Professor of Psychology, University of Illinois at Chicago</p>

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