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Black Swan or White?  Or Possibly Gray? Deconstructing Low Probability, High-Impact Events J. Davidson Frame, PhD, PMP, PMI Fellow
Black Swan Black Swan = Low probability, high-impact, unexpected event
It Began with the 2007 Book, The Black Swan Nicholas NassimTaleb, initially a financial trader who became a philosopher and essayist. Key points: ,[object Object]
 They are ill-equipped to deal with Black Swans
 Black Swans are trans-formational events that need to be reckoned with,[object Object]
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!
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.
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
Some Black Swan Events and Possible Responses A Whimsical View
THE EVENT
THE EVENT Risk Response Strategy
THE EVENT
Risk response strategy THE EVENT
The Personal Computer
The Personal Computer Seize the opportunity Risk Response Strategy
The Internet
The Internet Seize the opportunity Risk Response Strategy
THE EVENT
THE EVENT Risk Response Strategy
THE EVENT Bernard Madoff dressed as a Black Swan
THE EVENT Bernard Madoff dressed as a Black Swan Risk Response Strategy
THE EVENT MORAL DIMENSION OF BLACK SWANS Interesting point: Black Swans in finance are often linked to prevarication and illegal activities. Consider: ,[object Object]
 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 MADOFFBernard Madoff dressed as a Black Swan Risk Response Strategy
Dealing with Probabilities Black swans are low probability events… What does this mean?
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.
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
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
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.
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.
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?
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
http://www.risk-ed.org/pages/risk/asteroid_prob.htm Probability of Asteroid Impact
http://www.risk-ed.org/pages/risk/asteroid_prob.htm Probability of Asteroid Impact What are these quasi-objective probabilities telling us?
Weather.com Snow Prediction, Washington, DC, Winter 2010-2011  Snowfall prediction at: Snowfall prediction for:
Weather.com Snow Prediction, Washington, DC, Winter 2010-2011  Snowfall prediction at: Snowfall prediction for:
Weather.com Snow Prediction, Washington, DC, Winter 2010-2011  Snowfall prediction at: Snowfall prediction for:
Weather.com Snow Prediction, Washington, DC, Winter 2010-2011  Snowfall prediction at: Snowfall prediction for:
Weather.com Snow Prediction, Washington, DC, Winter 2010-2011  Snowfall prediction at: Snowfall prediction for:
Weather.com Snow Prediction, Washington, DC, Winter 2010-2011  Snowfall prediction at: Snowfall prediction for: What are these probabilities telling us?
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
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?
Probability of >5.0 earthquake, San Francisco, next 10 years
Probability of >5.0 earthquake, San Francisco, next 50 years
Probability of Earthquake in Richmond, VA, 10 Years
Probability of Earthquake in Richmond, VA, 50 Years
Probability of Earthquake in Richmond, VA, 50 Years On August 23, 2011, Richmond experienced an earthquake of 5.8 magnitude
Probability of >5.0 earthquake, NYC, next 10 years
Probability >5.0 earthquake in NYC, next 50 years
Earthquake Probability Website https://geohazards.usgs.gov/eqprob/2009/
The Need for a Non-traditional Look at Black Swans
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
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
Recent High-Impact Events Are They Black Swans?
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
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
Fukishima Daiichi Disaster a Black Swan?
Fukishima Daiichi Disaster a Black Swan? One person’s Black Swan is another’s predictable event
Toyota Car Acceleration Problem, Winter/Spring 2009/2010  Complaints of sudden acceleration of Toyota cars began in the Fall of 2009 ,[object Object]
 Sales stopped on affected models
 Executive testimony in Congress
 Enormous press coverageBy April 2010, acceleration problem was old news – reports of acceleration incidents suddenly stopped
Toyota Car Acceleration Problem, Winter/Spring 2009/2010  Complaints of sudden acceleration of Toyota cars began in the Fall of 2009 ,[object Object]
 Sales stopped on affected models
 Executive testimony in Congress

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Black swans

  • 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.
  • 4. They are ill-equipped to deal with Black Swans
  • 5.
  • 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
  • 11. THE EVENT Risk Response Strategy
  • 15. The Personal Computer Seize the opportunity Risk Response Strategy
  • 17. The Internet Seize the opportunity Risk Response Strategy
  • 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.
  • 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 MADOFFBernard 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
  • 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.
  • 64. Sales stopped on affected models
  • 65. Executive testimony in Congress
  • 66. Enormous press coverageBy April 2010, acceleration problem was old news – reports of acceleration incidents suddenly stopped
  • 67.
  • 68. Sales stopped on affected models
  • 69. Executive testimony in Congress
  • 70. Enormous press coveragePOSTSCRIPT 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.
  • 72. It had a huge impact on Toyota’s sales and reputation
  • 73.
  • 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.
  • 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