The failure of risk management : why it's broken and how to fix it

The failure of risk management : why it's broken and how to fix it /

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Bibliographic Details
Main Author: Hubbard, Douglas W., 1962- (Author)
Corporate Author: ProQuest (Firm)
Format: Electronic eBook
Language:English
Published: Hoboken, New Jersey : Wiley, 2020.
Edition:Second edition.
Subjects:
Risk management.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)

MARC

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100 1 |a Hubbard, Douglas W.,  |d 1962-  |e author. 
245 1 4 |a The failure of risk management :  |b why it's broken and how to fix it /  |c Douglas W. Hubbard. 
250 |a Second edition. 
264 1 |a Hoboken, New Jersey :  |b Wiley,  |c 2020. 
264 4 |c ©2020 
300 |a 1 online resource (xvii, 366 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b n  |2 rdamedia 
338 |a online resource  |b nc  |2 rdacarrier 
500 |a Includes index. 
504 |a Includes bibliographical references and index. 
505 0 0 |a Machine generated contents note:   |g pt. ONE   |t INTRODUCTION TO THE CRISIS --   |g ch. 1   |t Healthy Skepticism for Risk Management --   |t "Common Mode Failure" --   |t Key Definitions: Risk Management and Some Related Terms --   |t What Failure Means --   |t Scope and Objectives of This Book --   |g ch. 2   |t Summary of the Current State of Risk Management --   |t Short and Entirely-Too-Superficial History of Risk --   |t Current State of Risk Management in the Organization --   |t Current Risks and How They Are Assessed --   |g ch. 3   |t How Do We Know What Works? --   |t Anecdote: The Risk of Outsourcing Drug Manufacturing --   |t Why It's Hard to Know What Works --   |t Assessment of Self-Assessments --   |t Potential Objective Evaluations of Risk Management --   |t What We May Find --   |g ch. 4   |t Getting Started: A Simple Straw Man Quantitative Model --   |t Simple One-for-One Substitution --   |t Expert as the Instrument --   |t Quick Overview of "Uncertainty Math" --   |t Establishing Risk Tolerance --   |t Supporting the Decision: A Return on Mitigation --   |t Making the Straw Man Better --   |g pt. TWO   |t WHY IT'S BROKEN --   |g ch. 5   |t "Four Horsemen" of Risk Management: Some (Mostly) Sincere Attempts to Prevent an Apocalypse --   |t Actuaries --   |t War Quants: How World War II Changed Risk Analysis Forever --   |t Economists --   |t Management Consulting: How a Power Tie and a Good Pitch Changed Risk Management --   |t Comparing the Horsemen --   |t Major Risk Management Problems to Be Addressed --   |g ch. 6   |t Ivory Tower of Babel: Fixing the Confusion about Risk --   |t Frank Knight Definition --   |t Knight's Influence in Finance and Project Management --   |t Construction Engineering Definition --   |t Risk as Expected Loss --   |t Defining Risk Tolerance --   |t Defining Probability --   |t Enriching the Lexicon --   |g ch. 7   |t Limits of Expert Knowledge: Why We Don't Know What We Think We Know about Uncertainty --   |t Right Stuff: How a Group of Psychologists Might Save Risk Analysis --   |t Mental Math: Why We Shouldn't Trust the Numbers in Our Heads --   |t "Catastrophic" Overconfidence --   |t Mind of "Aces": Possible Causes and Consequences of Overconfidence --   |t Inconsistencies and Artifacts: What Shouldn't Matter Does --   |t Answers to Calibration Tests --   |g ch. 8   |t Worse Than Useless: The Most Popular Risk Assessment Method and Why It Doesn't Work --   |t Few Examples of Scores and Matrices --   |t Does That Come in "Medium"?: Why Ambiguity Does Not Offset Uncertainty --   |t Unintended Effects of Scales: What You Don't Know Can Hurt You --   |t Different but Similar-Sounding Methods and Similar but Different-Sounding Methods --   |g ch. 9   |t Bears, Swans and Other Obstacles to Improved Risk Management --   |t Algorithm Aversion and a Key Fallacy --   |t Algorithms versus Experts: Generalizing the Findings --   |t Note about Black Swans --   |t Major Mathematical Misconceptions --   |t We're Special: The Belief That Risk Analysis Might Work, but Not Here --   |g ch. 10   |t Where Even the Quants Go Wrong: Common and Fundamental Errors in Quantitative Models --   |t Survey of Analysts Using Monte Carlos --   |t Risk Paradox --   |t Financial Models and the Shape of Disaster: Why Normal Isn't So Normal --   |t Following Your Inner Cow: The Problem with Correlations --   |t Measurement Inversion --   |t Is Monte Carlo Too Complicated? --   |g pt. THREE   |t HOW TO FIX IT --   |g ch. 11   |t Starting with What Works --   |t Speak the Language --   |t Getting Your Probabilities Calibrated --   |t Using Data for Initial Benchmarks --   |t Checking the Substitution --   |t Simple Risk Management --   |g ch. 12   |t Improving the Model --   |t Empirical Inputs --   |t Adding Detail to the Model --   |t Advanced Methods for Improving Expert's Subjective Estimates --   |t Other Monte Carlo Tools --   |t Self-Examinations for Modelers --   |g ch. 13   |t Risk Community: Intra- and Extra-organizational Issues of Risk Management --   |t Getting Organized --   |t Managing the Model --   |t Incentives for a Calibrated Culture --   |t Extraorganizational Issues: Solutions beyond Your Office Building --   |t Practical Observations from Trustmark --   |t Final Thoughts on Quantitative Models and Better Decisions. 
533 |a Electronic reproduction.  |b Ann Arbor, MI  |n Available via World Wide Web. 
588 |a Description based on online resource; title from digital title page (viewed on March 10, 2020). 
650 0 |a Risk management. 
710 2 |a ProQuest (Firm) 
776 0 8 |i Print version:  |a Hubbard, Douglas W., 1962-  |t The failure of risk management  |b Second edition.  |d Hoboken, New Jersey : Wiley, 2020.  |z 9781119522034  |w (DLC) 2019051494 
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