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Stalking the Black Swan: Research and Decision Making in a World of Extreme Volatility resources

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notes Introduction . Nassim Nicholas Taleb, Th e Impact of the Highly Improbable e Black Swan: Th (New York: Random House, 2007). Regarding Taleb’s criticism of value at risk, a statistical risk management metric, see “Th e Jorion- Taleb Debate,” April 1997, at http:// DerivativesStrategy .com . . As Malcolm Gladwell puts it, “I think that the task of fi guring out how to combine the best of conscious deliberation and instinctive judgment is one of the great challenges of our time.” Gladwell, Blink: Th inking Without e Power of Th Th (New York: Little, Brown, 2005), 269. inking . Because of accelerating capacity growth in pro cessors, memory, and band- width, futurologist Ray Kurzweil predicts that the computational power of a typical personal computer will match that of the human brain sometime around 2020. Kurzweil, Th (New York: Vi- e Singularity Is Near: When Humans Transcend Biology king Penguin, 2005), 70. Chapter 1 . James Gleick, Chaos: Making a New Science (New York: Viking, 1987), 11– 31. . William A. Sherden, Th e Big Business of Buying and Selling e Fortune Sellers: Th Predictions (New York: Wiley, 1998), 31– 54. . American Meteorological Society, “Weather Analysis and Forecasting,” Bul- letin of the American Meteorological Society 88 (August 8, 2007). . A. J. Simmons and A. Hollingsworth, “Some Aspects of the Improvement in Skill of Numerical Weather Prediction,” Quarterly Journal of the Royal Meteorologi- cal Society 128 (2002): 647– 77. . Ibid., 648– 52. . Sherden, Fortune Sellers, 61– 72. For a more recent discussion with similar conclusions, see Michael F. Bryan and Linsey Molloy, “Mirror, Mirror, Who’s the Best Forecaster of Th Federal Reserve Bank of Cleveland Economic Commen- em All?” tary, March 13, 2007. . Tonis Vaga, Profi ting from Chaos: Using Chaos Th eory for Market Timing, Stock Selection, and Option Valuation (New York: McGraw- Hill, 1994), 33– 37.

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