• Power Blackouts, Space Shuttle Losses, Concorde Crashes, and the Chernobyl and Three Mile Island Accidents
• The Combination of Events
• The Problem Is the Human Element
• The Four Echoes Share the Same Four Phases
• The First Echo: Blackout of the Power Grid
• Management’s Role
• The First Echo: Findings
• Error State Elimination
• The Second Echo: Columbia/Challenger
• The Results of the Inquiry: Prior Knowledge
• The Second Echo: The Four Phases
• Management’s Responsibility
• Error State Elimination
• The Third Echo: Concorde Tires and SUVs
• Tire Failures: the Prior Knowledge
• The Third Echo: The Four Phases
• Management’s Responsibility
• Error State Elimination
• The Fourth Echo: Chernobyl
• The Chernobyl Accident: An Echo of Three Mile Island
• The Consequences
• Echoes of Three Mile Island
• The Causes
• Error State Elimination
• The Fourth Echo: The Four Phases
• Regulatory Environment and Practices
• Addressing Human Error
• Management Responsibilities
• Designing to Reduce Risk and the Role of Standards
• Conclusion and Echoes: Predicting the Unpredictable

• Learning from Near Misses and Prior Knowledge
• Problems in Quantifying Risk: Predicting the Risk for the Next Shuttle Mission
• Estimating a Possible Range of Likelihoods
• Learning from Experience: Maturity Models for Future Space Mission Risk
• Technology versus Technology
• Missiles Risks over London: The German Doodlebug
• Launching Missile Risk
• The Number of Tests Required
• Estimating the Risk of a Successful Attack and How Many Missiles We Must Fire
• Uncertainty in the Risk of Failing to Intercept
• What Risk Is There of a Missile Getting Through: Missing the Missile
• Predicting the Risk of Industrial Accidents: The Texas City Refinery Explosion
• From Lagging to Leading: Safety Analysis and Safety Culture
• Missing Near Misses
• What these Risk Estimates Tell Us: The Common Sense Echo

• What We Must Predict
• The Probability Linked to the Rate of Errors
• The Definition of Risk Exposure and the Level of Attainable Perfection
• Comparison to Conventional Social Science and Engineering Failure and Outcome Rate Formulations
• The Learning Probabilities and the PDFs
• The Initial Failure Rate and its Variation with Experience
• The ‘Best’ MERE Risk Values
• Maximum and Minimum Likely Outcome Rates
• Standard Engineering Reliability Models Compared to the MERE Result
• Future Event Estimates: The Past Predicts the Future
• Statistical Bayesian-Type Estimates: The Impact of Learning
• Maximum and Minimum Likelihood
• Comparison to Data: The Probability of Failure and Human Error
• Comparison of the MERE Result to Human Reliability Analysis
• Implications for Generalised Risk Prediction
• Conclusions: The Probable Human Risk

• A General Accident Theory: Error States and Safety Management
• The Physics of Errors
• The Learning Hypothesis and the General Accident Theory
• Observing Outcomes
• A Homage to Boltzmann: Information from the Grave
• The Concept of Depth of Experience and the Theory of Error States
• The Fundamental Postulates of Error State Theory
• The Information in Error States: Establishing the Risk Distribution
• The Exponential Distribution of Outcomes, Risk and Error States
• The Total Number of Outcomes
• The Observed Rate and the Minimum Number of Outcomes
• Accumulated Experience Measures and Learning Rates
• The Average Rate
• Analogy and Predictions: Statistical Error Theory and Learning Model Equivalence
• The Infl uence of Safety Management and Regulations: Imposing Order on Disorder
• The Risk of Losing a Ship
• Distribution Functions
• The Most Probable and Minimum Error Rate
• Learning Rates and Experience Intervals: The Universal Learning Curve
• Reducing the Risk of a Fatal Aircraft Accident: the Infl uence of Skill and Experience
• Conclusions: A New Approach

• Future Loss Rate Prediction: Ships and Tsunamis
• Predicted Insurance Rates for Shipping Losses: Historical Losses
• The Premium Equations
• Financial Risk: Dynamic Loss and Premium Investments
• Numerical Example
• Overall Estimates of Shipping Loss Fraction and Insurance Inspections
• The Loss Ratio: Deriving the Industrial Damage Curves
• Making Investment Decisions: Information Drawing from the Jar of Life
• Information Entropy and Minimum Risk
• Progress and Learning in Manufacturing
• Innovation in Technology for the Least Product Price and Cost: Reductions During Technological Learning
• Cost Reduction in Manufacturing and Production: Empirical Elasticity ‘Power Laws’ and Learning Rates
• A New General Formulation for Unit Cost Reduction in Competitive Markets: the Minimum Cost According to a Black-Scholes Formulation
• Universal Learning Curve: Comparison to the Usual Economic Power Laws
• The Learning Rate b-Value ‘Elasticity’ Exponent Evaluated
• Equivalent Average Total Cost b-Value Elasticity
• Profit Optimisation to Exceed Development Cost
• The Data Validate the Learning Theory
• Non-Dimensional UPC and Market Share
• Conclusions: Learning to Improve and Turning Risks into Profits

• Safety Management Systems: Creating Order Out of Disorder
• Workplace Safety: The Four Rights, Four Wrongs and Four Musts
• Acceptable Risk: Designing for Failure and Managing for Success
• Managing and Risk Matrices
• Organisational Factors and Learning
• A Practical ‘Safety Culture’ Example: The Fifth Echo
• Safety Culture and Safety Surveys: The Learning Paradox
• Never Happening Again: Perfect Learning
• Half a World Apart: Copying the Same Factors
• Using a Bucket: Errors in Mixing at the JCO Plant
• Using a Bucket: Errors in Mixing at the Kean Canyon Explosives Plant
• The Prediction and Management of Major Hazards: Learning from SMS Failures
• Learning Environments and Safety Cultures: The Desiderata of Desires
• Safety Performance Measures: Indicators and Balanced Scorecards
• Safety and Performance Indicators: Measuring the Good
• Human Error Rates Passing Red Lights, Runway Incursions and Near Misses
• Risk Informed Regulation and Degrees of Goodness: How Green is Green?
• Modelling and Predicting Event Rates and Learning Curves Using Accumulated Experience
• Using the Past to Predict the Future: How Good is Good?
• Reportable Events
• Scrams and Unplanned Shutdowns
• Common Cause Events and Latent Errors
• Performance Improvement: Case-by-Case
• Lack of Risk Reduction: Medical Adverse Events and Deaths
• New Data: Sentinel Events, Deaths and Blood Work
• Medication Errors in Health Care
• Organisational Learning and Safety Culture: the ‘H-Factor’
• Risk Indicator Data Analysis: A Case Study
• Meeting the Need to Measure Safety Culture: the Hard and the Soft Elements
• Creating Order from Disorder

• Perceptions and Predicting the Future: Risk Acceptance and Risk Avoidance
• Fear of the Unknown: The Success Journey into What We Do or Do Not Accept
• A Possible Explanation of Risk Perception: Comparisons of Road and Rail Transport
• How Do We Judge the Risk?
• Linking Complexity, Order, Information Entropy and Human Actions
• Response Times, Learning Data and the Universal Laws of Practice
• The Number and Distribution of Outcomes: Comparison to Data
• Risk Perception: Railways
• Risk Perception: Coal Mining
• Risk Perception: Nuclear Power in Japan
• Risk Perception: Rare Events and Risk Rankings
• Predicting the Future Number of Outcomes
• A Worked Example: Searching out and Analysing Data for Oil Spills
• Typical Worksheet
• Plotting the Data
• Fitting a Learning Curve
• Challenging Zero Defects
• Comparison of Oil Spills to other Industries
• Predicting the Future: the Probability and Number of Spills
• Observations on this Oil Spill Case
• Knowing What We Do Not Know: Fear and Managing the Risk of the Unknown
• White and Black Paradoxes: Known Knowns and Unknown Unknowns
• The Probability of the Unknowns: Learning from What We Know
• The Existence of the Unknown: Failures in High Reliability Systems
• The Power of Experience: Facing Down the Fear of the Unknown
• Terrorism, Disasters and Pandemics: Real, Acceptable and Imaginary Risks
• Estimating Personal Risk of Death: Pandemics and Infectious Diseases
• Sabotage: Vulnerabilities, Critical Systems and the Reliability of Security Systems
• What Is the Risk?
• The Four Quadrants: Implications of Risk for Safety Management Systems

• Where We Have Come From
• What We Have Learned
• What We Have Shown
• Legal, Professional and Corporate Implications for the Individual
• Just Give Me the Facts
• Where We Are Going

Index