The document discusses the concept of 'black swan' events, defined as low probability, high-impact, and unexpected occurrences, highlighting their relevance in project management and finance. It critiques traditional views on risk assessment and emphasizes the importance of identifying and preparing for such unpredictable events. Examples include the 2008 financial crisis and the Toyota car acceleration problem, illustrating how perception of these events can vary and the need for flexible strategies in managing risk.

1. Black Swan or White? Or Possibly Gray?Deconstructing Low Probability, High-Impact EventsJ. Davidson Frame, PhD, PMP, PMI Fellow

2. Black SwanBlack Swan = Low probability, high-impact, unexpected event

3. It Began with the 2007 Book, The Black SwanNicholas 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 withLikelihood-Impact Matrix and Black SwansHighLikelihoodMedLowHighMedLowImpact

6. Black Swans and Project ManagementMurphy’s Law is a reflection of project management concern with Black SwansIf something can go wrong, it willUnk-Unks and Black SwansProject managers have had to contend with unknown-unknowns for ages, particularly on major programsUnk-unks can be viewed as a subset of Black SwansIn 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 reserveOOPs!

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 ChallengeIdentifying the Black Swan and its impactAvoiding self-delusionHandling Unk-UnksDetermining likelihoodAvoiding self-delusionGetting a handle on the likelihood of rare eventsDealing with subjective and quasi-objective probabilitiesIdentifying a risk response strategy

9. Some Black Swan Events and Possible ResponsesA Whimsical View

10. THE EVENT

11. THE EVENTRisk Response Strategy

12. THE EVENT

13. Risk response strategyTHE EVENT

14. The Personal Computer

15. The Personal ComputerSeize the opportunityRisk Response Strategy

16. The Internet

17. The InternetSeize the opportunityRisk Response Strategy

18. THE EVENT

19. THE EVENTRisk Response Strategy

20. THE EVENTBernard Madoff dressed as a Black Swan

21. THE EVENTBernard Madoff dressed as a Black SwanRisk Response Strategy

22. THE EVENTMORAL DIMENSION OF BLACK SWANSInteresting 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 MADOFFBernard Madoff dressed as a Black SwanRisk Response Strategy

29. Dealing with ProbabilitiesBlack swans are low probability events… What does this mean?

30. ProbabilityObjective probability:An event’s long-run relative frequencye.g., Probability of drawing a spade from a deck of cards = 13/52 = 0.25Subjective probability:Personal estimate of whether an event will occur, e.g., the probability that the Washington Redskins will win the Super Bowl.

31. Objective ProbabilitiesProbability 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 ProbabilitiesProbability 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 companiesThese probabilities are easy to interpret

33. Quasi-objective ProbabilitiesMany 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 ProbabilitiesMany 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 ConundrumsProbability 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 todayHow is this computed?What does this mean?

36. Probability ConundrumsProbability 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 todayHow is this computed?What does this mean?I understand the following statement: 15% of the universe’s planets have intelligent lifeI 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.htmProbability of Asteroid Impact

38. http://www.risk-ed.org/pages/risk/asteroid_prob.htmProbability of Asteroid ImpactWhat 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 nuclearweapons 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 nuclearweapons laboratoryWhat 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 YearsOn 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 Websitehttps://geohazards.usgs.gov/eqprob/2009/

55. The Need for a Non-traditional Look at Black Swans

56. Traditional View: Our Expected Value WorldEV 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 = $131,500VERDICT: BID ON THE PROJECT

57. Fitting Black Swans into an Expected Value WorldBlack Swan: sudden regulatory change leads to lossInclusion 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 EventsAre They Black Swans?

59. Fukishima Daiichi Earthquake and Tsunami, March 201115 active nuclear sites along the coastline, each with multiple reactors, each in a geologically active zoneThe Fukishima nuclear plant survived the earthquake and tsunami – core meltdown was tied to flooded generators – without electric power, cores overheatedFukishima Daiichi

60. CaliforniaTwo ocean-side nuclear plantsSan Onofre (between San Diego and LA)Diablo Canyon (San Luis Obispo)The good newsGeological 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 highThe backup gravity cooling systems and diesel power generators are less vulnerable to tsunami impactDiablo Canyon sits on top of an 86 foot bluffSan Luis ObispoSan 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 coverageBy 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