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In his thought-provoking book, The Black Swan , Nassim Nichloas Taleb warns us that in managing risk, we are asking for trouble if we ignore dealing with low-probability, high-impact events --......

In his thought-provoking book, The Black Swan , Nassim Nichloas Taleb warns us that in managing risk, we are asking for trouble if we ignore dealing with low-probability, high-impact events -- so-called Black Swans. Coming from the financial sector, he focuses particularly on surprise events that can serve to undo banks, investments houses, and even whole economies.
In his presentation, Dr. J. Davidson Frame examines the premises of Black Swan events to see the extent to which they are truly unpredictable and unmanageable. Through the analysis of recent, high-visibility Black Swan events (including the Toyota car-acceleration event of Winter/Spring 2010; the Fukushima tsunami and nuclear plant disaster of March 2011; and the China Bullet Train crash of July 2011) he shows how risk identification, diligent risk impact analysis, careful risk response planning, and diligent risk monitoring and control can help you handle Black Swans, as well as more conventional risk scenarios, in the management of projects.

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  • 1. Black Swan or White? Or Possibly Gray?
    Deconstructing Low Probability, High-Impact Events
    J. Davidson Frame, PhD, PMP, PMI Fellow
  • 2. Black Swan
    Black Swan = Low probability, high-impact, unexpected event
  • 3. It Began with the 2007 Book, The Black Swan
    Nicholas NassimTaleb, initially a financial trader who became a philosopher and essayist.
    Key points:
    • Most statisticians and finance specialists focus on statistically predictable scenarios – i.e., governed by the normal distribution
    • 4. They are ill-equipped to deal with Black Swans
    • 5. Black Swans are trans-formational events that need to be reckoned with
  • Likelihood-Impact Matrix and Black Swans
    High
    Likelihood
    Med
    Low
    High
    Med
    Low
    Impact
  • 6. Black Swans and Project Management
    Murphy’s Law is a reflection of project management concern with Black Swans
    If something can go wrong, it will
    Unk-Unks and Black Swans
    Project managers have had to contend with unknown-unknowns for ages, particularly on major programs
    Unk-unks can be viewed as a subset of Black Swans
    In project management, the principal approach to handling unk-unks is to carry out good risk identification, understand risk impacts, and implement good risk response – often through management reserve
    OOPs!
  • 7. The Black Swan Metaphor in Philosophy
    Until the 17th century, Europeans only encountered white swans. They used black swans as an example of something that is plausible but does not exist. However, in 1697, black swans were discovered in Western Australia – they really do exist.
    Philosophers used this discovery to illustrate the problem of induction: Even though I observe 10,000 white swans, this does not mean that the next swan I observe will be white.
  • 8. The Black Swan Challenge
    Identifying the Black Swan and its impact
    Avoiding self-delusion
    Handling Unk-Unks
    Determining likelihood
    Avoiding self-delusion
    Getting a handle on the likelihood of rare events
    Dealing with subjective and quasi-objective probabilities
    Identifying a risk response strategy
  • 9. Some Black Swan Events and Possible Responses
    A Whimsical View
  • 10. THE EVENT
  • 11. THE EVENT
    Risk Response Strategy
  • 12. THE EVENT
  • 13. Risk response strategy
    THE EVENT
  • 14. The Personal Computer
  • 15. The Personal Computer
    Seize the opportunity
    Risk Response Strategy
  • 16. The Internet
  • 17. The Internet
    Seize the opportunity
    Risk Response Strategy
  • 18. THE EVENT
  • 19. THE EVENT
    Risk Response Strategy
  • 20. THE EVENT
    Bernard Madoff dressed as a Black Swan
  • 21. THE EVENT
    Bernard Madoff dressed as a Black Swan
    Risk Response Strategy
  • 22. THE EVENT
    MORAL DIMENSION OF BLACK SWANS
    Interesting point: Black Swans in finance are often linked to prevarication and illegal activities. Consider:
    • Bernard Ebbers, Countrywide
    • 23. Kenneth Lay, Enron
    • 24. Distorted ratings of securities by credit rating firms
    • 25. Rogue traders, e.g., Nick Leeson (Barings) and KwekuAbadoli (UBS)
    • 26. Mortage sales staff pushing sub-prime loans onto unqualified borrowers
    • 27. Security sales staff pawning over-rated securities to investors
    • 28. BERNARD MADOFF
    Bernard Madoff dressed as a Black Swan
    Risk Response Strategy
  • 29. Dealing with Probabilities
    Black swans are low probability events… What does this mean?
  • 30. Probability
    Objective probability:
    An event’s long-run relative frequency
    e.g., Probability of drawing a spade from a deck of cards = 13/52 = 0.25
    Subjective probability:
    Personal estimate of whether an event will occur, e.g., the probability that the Washington Redskins will win the Super Bowl.
  • 31. Objective Probabilities
    Probability of drawing a spade in a random drawing from a deck of cards (Pr = 13/52 = 0.25)
    Probability of drawing an Ace of Spades in a random drawing from a deck of cards (Pr = 1/52 = 0.01923)
    Probability of drawing a full house hand in a random drawing from a deck of cards (Pr = 0.001441)
    All probabilities associated with actuarial tables maintained by insurance companies
  • 32. Objective Probabilities
    Probability of drawing a spade in a random drawing from a deck of cards (Pr = 13/52 = 0.25)
    Probability of drawing an Ace of Spades in a random drawing from a deck of cards (Pr = 1/52 = 0.01923)
    Probability of drawing a full house hand in a random drawing from a deck of cards (Pr = 0.001441)
    All probabilities associated with actuarial tables maintained by insurance companies
    These probabilities are easy to interpret
  • 33. Quasi-objective Probabilities
    Many of the probabilities we deal with in our daily lives are quasi-objective, lying somewhere between objective and subjective probabilities. A priori and empirically-determined frequency counts do not exist, or exist partially.
    Examples include the probability of snowfall and the probability of the economy slipping into a recession.
  • 34. Quasi-objective Probabilities
    Many of the probabilities we deal with in our daily lives are quasi-objective, lying somewhere between objective and subjective probabilities. A priori and empirically-determined frequency counts do not exist, or exist partially.
    Examples include the probability of snowfall and the probability of the economy slipping into a recession.
    While objective probabilities can be readily interpreted, the interpretation of quasi-objective and subjective probabilities can be vague.
  • 35. Probability Conundrums
    Probability of intelligent life on another planet?
    How is this computed?
    What does this mean?
    Probability of earth being struck by an asteroid within 100 years?
    How is this computed?
    What does this mean?
    Probability that it will rain today
    How is this computed?
    What does this mean?
  • 36. Probability Conundrums
    Probability of intelligent life on another planet?
    How is this computed?
    What does this mean?
    Probability of earth being struck by an asteroid within 100 years?
    How is this computed?
    What does this mean?
    Probability that it will rain today
    How is this computed?
    What does this mean?
    I understand the following statement: 15% of the universe’s planets have intelligent life
    I don’t understand: there is a 15% chance of intelligent life in the universe, outside earth
  • 37. http://www.risk-ed.org/pages/risk/asteroid_prob.htm
    Probability of Asteroid Impact
  • 38. http://www.risk-ed.org/pages/risk/asteroid_prob.htm
    Probability of Asteroid Impact
    What are these quasi-objective probabilities telling us?
  • 39. Weather.com Snow Prediction, Washington, DC, Winter 2010-2011
    Snowfall prediction at:
    Snowfall prediction for:
  • 40. Weather.com Snow Prediction, Washington, DC, Winter 2010-2011
    Snowfall prediction at:
    Snowfall prediction for:
  • 41. Weather.com Snow Prediction, Washington, DC, Winter 2010-2011
    Snowfall prediction at:
    Snowfall prediction for:
  • 42. Weather.com Snow Prediction, Washington, DC, Winter 2010-2011
    Snowfall prediction at:
    Snowfall prediction for:
  • 43. Weather.com Snow Prediction, Washington, DC, Winter 2010-2011
    Snowfall prediction at:
    Snowfall prediction for:
  • 44. Weather.com Snow Prediction, Washington, DC, Winter 2010-2011
    Snowfall prediction at:
    Snowfall prediction for:
    What are these probabilities telling us?
  • 45. 100-Year Flood
    “Although we are situated in a desert, we have occasional torrential rains in the mountains that cause major floods. In designing our buildings, we are required to design them to cope with the 100-year flood … We’ve had two 100-year floods in the past 30 years.”
    Civil engineer at a US nuclear
    weapons laboratory
  • 46. 100-Year Flood
    “Although we are situated in a desert, we have occasional torrential rains in the mountains that cause major floods. In designing our buildings, we are required to design them to cope with the 100-year flood … We’ve had two 100-year floods in the past 30 years.”
    Civil engineer at a US nuclear
    weapons laboratory
    What does a 100-year flood mean?
  • 47. Probability of >5.0 earthquake, San Francisco, next 10 years
  • 48. Probability of >5.0 earthquake, San Francisco, next 50 years
  • 49. Probability of Earthquake in Richmond, VA, 10 Years
  • 50. Probability of Earthquake in Richmond, VA, 50 Years
  • 51. Probability of Earthquake in Richmond, VA, 50 Years
    On August 23, 2011, Richmond experienced an earthquake of 5.8 magnitude
  • 52. Probability of >5.0 earthquake, NYC, next 10 years
  • 53. Probability >5.0 earthquake in NYC, next 50 years
  • 54. Earthquake Probability Website
    https://geohazards.usgs.gov/eqprob/2009/
  • 55. The Need for a Non-traditional Look at Black Swans
  • 56. Traditional View: Our Expected Value World
    EV analysis is used heavily in business decision-making.
    Example: Bid decision
    Target revenue: $2,000,000, Target costs: $1,450,000
    Anticipated profit (if won): $550,000
    Proposal development costs: $50,000
    We believe that two other companies are bidding on the project, so our a priori probability of winning it is 33%.
    Expected monetary value = EV(Gain) – EV(Loss)
    EMV = $500,000*0.33 - $50,000*0.67 = $165,000 - $33,500
    = $131,500
    VERDICT: BID ON THE PROJECT
  • 57. Fitting Black Swans into an Expected Value World
    Black Swan: sudden regulatory change leads to loss
    Inclusion of a low probability but large Black Swan loss is not meaningful when dealing with expected value analysis – while the expected value of the loss may be small, if it occurs it may put you out of business
  • 58. Recent High-Impact Events
    Are They Black Swans?
  • 59. Fukishima Daiichi Earthquake and Tsunami, March 2011
    15 active nuclear sites along the coastline, each with multiple reactors, each in a geologically active zone
    The Fukishima nuclear plant survived the earthquake and tsunami – core meltdown was tied to flooded generators – without electric power, cores overheated
    Fukishima Daiichi
  • 60. California
    Two ocean-side nuclear plants
    San Onofre (between San Diego and LA)
    Diablo Canyon (San Luis Obispo)
    The good news
    Geological factors are likely to limit earthquakes to 7.5 level Richter scale (1/30th the force of the Fukishima Daiichi earthquake)
    The highest recorded tsunami in California wave was 7 ft high
    The backup gravity cooling systems and diesel power generators are less vulnerable to tsunami impact
    Diablo Canyon sits on top of an 86 foot bluff
    San Luis Obispo
    San Onofre
  • 61. Fukishima Daiichi Disaster a Black Swan?
  • 62. Fukishima Daiichi Disaster a Black Swan?
    One person’s Black Swan is another’s predictable event
  • 63. Toyota Car Acceleration Problem, Winter/Spring 2009/2010
    Complaints of sudden acceleration of Toyota cars began in the Fall of 2009
    • 9 million car recalls
    • 64. Sales stopped on affected models
    • 65. Executive testimony in Congress
    • 66. Enormous press coverage
    By April 2010, acceleration problem was old news – reports of acceleration incidents suddenly stopped
  • 67. Toyota Car Acceleration Problem, Winter/Spring 2009/2010
    Complaints of sudden acceleration of Toyota cars began in the Fall of 2009
    • 9 million car recalls
    • 68. Sales stopped on affected models
    • 69. Executive testimony in Congress
    • 70. Enormous press coverage
    POSTSCRIPT
    In February 2011, the NHTSA and NASA concluded a 10-month study. Found no problem with electronic controls. Of 58 “problem cars” studied, only one entailed mechanical problem (with the pedal). Conclusion: Problems caused by driver error.
    By April 2010, acceleration problem was old news – reports of acceleration incidents suddenly stopped
  • 71. Toyota Car Acceleration Problem a Black Swan?
    Yes, it was a Black Swan.
    • It came from nowhere – NOBODY ANTICIPATED IT
    • 72. It had a huge impact on Toyota’s sales and reputation
    • 73. It was a product of mass hysteria
  • China Bullet Train Accident, July 2011
    China bullet train plows into second stalled bullet train in Wenzhou
    40 killed, 177 injured
    Two problems
    Stopped train stalled by lightning strike
    Sensors failed – signal stayed green instead of turning red
    Bullet train system had been strongly criticized from the beginning
    No fatalities on the Japanese bullet train in 50 years
  • 74. Was the China Bullet Train Accident a Black Swan?
    It was a Black Swan if it is inconceivable to you that a government would launch a multi-billion dollar high tech project without implementing the most basic safety mesures
    It was not a Black Swan if you are aware that the rush to field a high tech project ASAP, without proper safety preparations, gives rise to an accident waiting to happen
  • 75. Was the China Bullet Train Accident a Black Swan?
    It was a Black Swan if it is inconceivable to you that a government would launch a multi-billion dollar high tech project without implementing the most basic safety mesures
    One person’s Black Swan is another’s predictable event
    It was not a Black Swan if you are aware that the rush to field a high tech project ASAP, without proper safety preparations, gives rise to an accident waiting to happen
  • 76. Global Financial Meltdown, Autumn 2008
    October 2007: Dow Jones peaked at 14,000; by March 2009, Dow Jones dropped to 6,600
    Subprime loan problems emerge during the Summer of 2007
    Lehman Brothers declares bankruptcy, September 15, 2008
    Paulson and Bernanke propose $700 billion bailout on September 18, 2008
    Emergency Economic Stabilization Act signed on October 3, 2008 – TARP is launched
  • 77. Global Financial Meltdown A Black Swan?
    While the demise of Lehman Brothers in September 2008 shocked the global financial community, since 2006, plenty of credible players recognized the inevitable popping of the asset bubble. The big question revolved around timing.
    David Smick (The World Is Curved, 2008) and Paul Krugman (Return to Depression Economics, 2008) described accurately how the meltdown would play out long before Lehman’s demise.
  • 78. Global Financial Meltdown A Black Swan?
    Perhaps the only American genuinely surprised by the meltdown was former Fed Chair, Alan Greenspan
    While the demise of Lehman Brothers in September 2008 shocked the global financial community, since 2006, plenty of credible players recognized the inevitable popping of the asset bubble. The big question revolved around timing.
    David Smick (The World Is Curved, 2008) and Paul Krugman (Return to Depression Economics, 2008) described accurately how the meltdown would play out long before Lehman’s demise.
  • 79. Conclusion
  • 80. Q&A Last Words
    Q: Do Black Swans really exist?
    A: Absolutely. A key component is the element of surprise. The Toyota car acceleration problem is a true Black Swan. The 2008 financial crisis is not.
    Q: Does it make a difference whether we face a predictable low probability, high-impact event vs. a Black Swan?
    A: Yes. The former can be addressed through contingency planning and reserves, while the latter is handled with management reserves and a flexible outlook. The accessibility of the sea to the Fukishima power generators reflects bad planning – the power generators could have been located inland.
    Q: Any practical guidance on surfacing Black Swans?
    A: Yes. Be on the look out for: 1) self-delusion and 2) lying
  • 81. Q&A Last Words
    Q: Do Black Swans really exist?
    A: Absolutely. A key component is the element of surprise. The Toyota car acceleration problem is a true Black Swan. The 2008 financial crisis is not.
    Q: Does it make a difference whether we face a predictable low probability, high-impact event vs. a Black Swan?
    A: Yes. The former can be addressed through contingency planning and reserves, while the latter is handled with management reserves and a flexible outlook. The accessibility of the sea to the Fukishima power generators reflects bad planning – the power generators could have been located inland.
    Q: Any practical guidance on surfacing Black Swans?
    A: Yes. Be on the look out for: 1) self-delusion and 2) lying
    Remember
    One person’s Black Swan is another’s predictable event
  • 82. Recommended Reading for Skeptics
    Michael Lewis, The Big Short: Inside the Doomsday Machine (2009)
    Michael Lewis, Boomerang: Travels in the New Third World (2011)
    David H. Friedman, Wrong: Why Experts Keep Failing Us – and How to Know When to Stop Trusting Them (2010)
    Future Reading
    (To be published, Summer 2012)
    J. Davidson Frame, Framing Decisions: Decision-making that Accounts for Irrationality, People and Constraints, Jossey-Bass, 2012
  • 83. J. Davidson Frame, PhD, PMP
    University of Management & Technology
    1901 Ft. Myer Drive, Suite 700
    Arlington, Va 22209
    davidson.frame@umtweb.edu
    www.umtweb.edu