The Black Swan: The Impact of the Highly Improbable

Cumulative advantage and its consequences on social fairness:review in diprete et al. (2006). See also Brookes-Gun and Duncan (1994), Broughton and Mills (1980), Dannefer (2003), Donhardt (2004), Hannon (2003), and Huber (1998). For how it may explain precocity, see Elman and O’Rand (2004).

Concentration and fairness in intellectual careers:cole and cole (1973), cole (1970), conley (1999), faia (1975), seglen (1992), redner (1998), lotka (1926), fox and Kochanowski (2004), and Huber (2002).

Winner take all:rosen (1981), frank (1994), frank and Cook (1995), and Attewell (2001).

Arts:bourdieu (1996), Taleb (2004e).

Wars:war is concentrated in an Extremistan manner: Lewis Fry Richardson noted last century the uneveness in the distribution of casualties (Richardson [I960]).

Modern wars:arkush and Allen (2006). In the study of the Maori, the pattern of fighting with clubs was sustainable for many centuries—modern tools cause 20,000 to 50,000 deaths a year. We are simply not made for technical warfare. For an anecdotal and causative account of the history of a war, see Ferguson (2006).

S&P 500: See Rosenzweig (2006).

The long tail:anderson (2006).

Cognitive diversity:see page (2007). for the effect of the Internet on schools, see Han et al. (2006).

Cascades:see Schelling (1971, 1978) and Watts (2002). For information cascades in economics, see Bikhchandani, Hirshleifer, and Welch (1992) and Shiller (1995). See also Surowiecki (2004).

Fairness:some researchers, like Frank (1999), see arbitrary and random success by others as no different from pollution, which necessitates the enactment of a tax. De Vany, Taleb, and Spitznagel (2004) propose a market-based solution to the problem of allocation through the process of voluntary self-insurance and derivative products. Shiller (2003) proposes cross-country insurance.

The mathematics of preferential attachment:this argument pitted mandelbrot against the cognitive scientist Herbert Simon, who formalized Zipfs ideas in a 1955 paper (Simon [1955]), which then became known as the Zipf-Simon model. Hey, you need to allow for people to fall from favor!

Concentration:price (1970). simon’s “Zipf derivation,” Simon (1955). More general bib-liometrics, see Price (1976) and Glänzel (2003).

Creative destruction revisited:see schumpeter (1942).

Networks: Barabási and Albert (1999), Albert and Barabási (2000), Strogatz (2001, 2003), Callaway et al. (2000), Newman et al. (2000), Newman, Watts, and Strogatz (2000), Newman (2001), Watts and Strogatz (1998), Watts (2002, 2003), and Ama-ral et al. (2000). It supposedly started with Milgram (1967). See also Barbour and Reinert (2000), Barthélémy and Amaral (1999). See Boots and Sasaki (1999) for infections. For extensions, see Bhalla and Iyengar (1999). Resilence, Cohen et al. (2000), Barabási and Bonabeau (2003), Barabási (2002), and Banavar et al. (2000). Power laws and the Web, Adamic and Huberman (1999) and Adamic (1999). Statistics of the Internet: Huberman (2001), Willinger et al. (2004), and Faloutsos, Falout-sos, and Faloutsos (1999). For DNA, see Vogelstein et al. (2000).

Self-organized criticality:bak (1996).

Pioneers of fat tails:for wealth, pareto (1896), yule (1925,1944). less of a pioneer Zipf (1932, 1949). For linguistics, see Mandelbrot (1952).

Pareto:see bouvier (1999).

Endogenous vs. exogenous:sornette et al. (2004).

Sperber’s work: Sperber (1996a, 1996b, 1997).

Regression: If you hear the phrase least square regression, you should be suspicious about the claims being made. As it assumes that your errors wash out rather rapidly, it underestimates the total possible error, and thus overestimates what knowledge one can derive from the data.

The notion of central limit:very misunderstood: it takes a long time to reach the central limit—so as we do not live in the asymptote, we’ve got problems. All various random variables (as we started in the example of Chapter 16 with a +1 or –1, which is called a Bernouilli draw) under summation (we did sum up the wins of the 40 tosses) be: come Gaussian. Summation is key here, since we are considering the results of adding up the 40 steps, which is where the Gaussian, under the first and second central assumptions becomes what is called a “distribution.” (A distribution tells you how you are likely to have your outcomes spread out, or distributed.) However, they may get there at different speeds. This is called the central limit theorem: if you add random variables coming from these individual tame jumps, it will lead to the Gaussian.