Black Swan or White?  Or Possibly Gray?<br />Deconstructing Low Probability, High-Impact Events<br />J. Davidson Frame, Ph...
Black Swan<br />Black Swan = Low probability, high-impact, unexpected event<br />
It Began with the 2007 Book, The Black Swan<br />Nicholas NassimTaleb, initially a financial trader who became a philosoph...
 They are ill-equipped to deal with Black Swans
 Black Swans are trans-formational events that need to be reckoned with</li></li></ul><li>Likelihood-Impact Matrix and Bla...
Black Swans and Project Management<br />Murphy’s Law is a reflection of project management concern with Black Swans<br />I...
The Black Swan Metaphor in Philosophy<br />	Until the 17th century, Europeans only encountered white swans. They used blac...
The Black Swan Challenge<br />Identifying the Black Swan and its impact<br />Avoiding self-delusion<br />Handling Unk-Unks...
Some Black Swan Events and Possible Responses<br />A Whimsical View<br />
THE EVENT<br />
THE EVENT<br />Risk Response Strategy<br />
THE EVENT<br />
Risk response strategy<br />THE EVENT<br />
The Personal Computer<br />
The Personal Computer<br />Seize the opportunity<br />Risk Response Strategy<br />
The Internet<br />
The Internet<br />Seize the opportunity<br />Risk Response Strategy<br />
THE EVENT<br />
THE EVENT<br />Risk Response Strategy<br />
THE EVENT<br />Bernard Madoff dressed as a Black Swan<br />
THE EVENT<br />Bernard Madoff dressed as a Black Swan<br />Risk Response Strategy<br />
THE EVENT<br />MORAL DIMENSION OF BLACK SWANS<br />Interesting point: Black Swans in finance are often linked to prevarica...
 Kenneth Lay, Enron
 Distorted ratings of securities by credit rating firms
 Rogue traders, e.g., Nick Leeson (Barings) and KwekuAbadoli (UBS)
Mortage sales staff pushing sub-prime loans onto unqualified borrowers
 Security sales staff pawning over-rated securities to investors
 BERNARD MADOFF</li></ul>Bernard Madoff dressed as a Black Swan<br />Risk Response Strategy<br />
Dealing with Probabilities<br />Black swans are low probability events… What does this mean?<br />
Probability<br />Objective probability:<br />An event’s long-run relative frequency<br />e.g., Probability of drawing a sp...
Objective Probabilities<br />Probability of drawing a spade in a random drawing from a deck of cards (Pr = 13/52 = 0.25)<b...
Objective Probabilities<br />Probability of drawing a spade in a random drawing from a deck of cards (Pr = 13/52 = 0.25)<b...
Quasi-objective Probabilities<br />Many of the probabilities we deal with in our daily lives are quasi-objective, lying so...
Quasi-objective Probabilities<br />Many of the probabilities we deal with in our daily lives are quasi-objective, lying so...
Probability Conundrums<br />Probability of intelligent life on another planet?<br />How is this computed?<br />What does t...
Probability Conundrums<br />Probability of intelligent life on another planet?<br />How is this computed?<br />What does t...
http://www.risk-ed.org/pages/risk/asteroid_prob.htm<br />Probability of Asteroid Impact<br />
http://www.risk-ed.org/pages/risk/asteroid_prob.htm<br />Probability of Asteroid Impact<br />What are these quasi-objectiv...
Weather.com Snow Prediction, Washington, DC, Winter 2010-2011 <br />Snowfall prediction at:<br />Snowfall prediction for:<...
Weather.com Snow Prediction, Washington, DC, Winter 2010-2011 <br />Snowfall prediction at:<br />Snowfall prediction for:<...
Weather.com Snow Prediction, Washington, DC, Winter 2010-2011 <br />Snowfall prediction at:<br />Snowfall prediction for:<...
Weather.com Snow Prediction, Washington, DC, Winter 2010-2011 <br />Snowfall prediction at:<br />Snowfall prediction for:<...
Weather.com Snow Prediction, Washington, DC, Winter 2010-2011 <br />Snowfall prediction at:<br />Snowfall prediction for:<...
Weather.com Snow Prediction, Washington, DC, Winter 2010-2011 <br />Snowfall prediction at:<br />Snowfall prediction for:<...
100-Year Flood<br />“Although we are situated in a desert, we have occasional torrential rains in the mountains that cause...
100-Year Flood<br />“Although we are situated in a desert, we have occasional torrential rains in the mountains that cause...
Probability of >5.0 earthquake, San Francisco, next 10 years<br />
Probability of >5.0 earthquake, San Francisco, next 50 years<br />
Probability of Earthquake in Richmond, VA, 10 Years<br />
Probability of Earthquake in Richmond, VA, 50 Years<br />
Probability of Earthquake in Richmond, VA, 50 Years<br />On August 23, 2011, Richmond experienced an earthquake of 5.8 mag...
Probability of >5.0 earthquake, NYC, next 10 years<br />
Probability >5.0 earthquake in NYC, next 50 years<br />
Earthquake Probability Website<br />https://geohazards.usgs.gov/eqprob/2009/<br />
The Need for a Non-traditional Look at Black Swans<br />
Traditional View: Our Expected Value World<br />EV analysis is used heavily in business decision-making.<br />	Example: Bi...
Fitting Black Swans into an Expected Value World<br />Black Swan: sudden regulatory change leads to loss<br />Inclusion of...
Recent High-Impact Events<br />Are They Black Swans?<br />
Fukishima Daiichi Earthquake and Tsunami, March 2011<br />15 active nuclear sites along the coastline, each with multiple ...
California<br />Two ocean-side nuclear plants<br />San Onofre (between San Diego and LA)<br />Diablo Canyon (San Luis Obis...
Fukishima Daiichi Disaster a Black Swan?<br />
Fukishima Daiichi Disaster a Black Swan?<br />One person’s Black Swan is another’s predictable event<br />
Toyota Car Acceleration Problem, Winter/Spring 2009/2010 <br />Complaints of sudden acceleration of Toyota cars began in t...
 Sales stopped on affected models
 Executive testimony in Congress
 Enormous press coverage</li></ul>By April 2010, acceleration problem was old news – reports of acceleration incidents sud...
Toyota Car Acceleration Problem, Winter/Spring 2009/2010 <br />Complaints of sudden acceleration of Toyota cars began in t...
 Sales stopped on affected models
 Executive testimony in Congress
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Black swans

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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 -- 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.

Published in: Economy & Finance, Business

Black swans

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

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