I received helpful comments from the voracious intellectual Paul Solman
(who went through the manuscript with a microscope). I owe a lot to Phil
Rosenczweig, Avishai Margalit, Peter Forbes, Michael Schrage, Driss Ben
Brahim, Vinay Pande, Antony Van Couvering, Nicholas Vardy, Brian Hinchcliffe,
Aaron Brown, Espen Haug, Neil Chriss, Zvika Afik, Shaiy Pilpel, Paul
Kedrosky, Reid Bernstein, Claudia Schmid, Jay Leonard, Tony Glickman,
Paul Johnson, Chidem Kurdas (and the NYU Austrian economists), Charles
Babbitt, plus so many anonymous persons I have forgotten about* . . .
Ralph Gomory and Jesse Ausubel of the Sloan Foundation run a research
funding program called the Known, the Unknown, and the Unknowable.
They offered their moral and financial help for the promotion
of my ideas—I took the invaluable moral option. I also thank my business
partners, coauthors, and intellectual associates: Espen Haug, Mark Spitznagel,
Beno?t Mandelbrot, Tom Witz, Paul Wilmott, Avital Pilpel, and
Emanuel Derman. I also thank John Brockman and Katinka Matson for
making this book possible, and Max Brockman for his comments on the
draft. I thank Cindy, Sarah, and Alexander for their tolerance. In addition,
Alexander helped with the graphs and Sarah worked on the bibliography.
I tried to give my editor, Will Murphy, the impression of being an unbearably
stubborn author, only to discover that I was fortunate that he
was an equally stubborn editor (but good at hiding it). He protected me
from the intrusions of the standardizing editors. They have an uncanny
ability to inflict maximal damage by breaking the internal rhythm of one's
prose with the minimum of changes. Will M. is also the right kind of party
* I lost his business card, but would like to warmly thank a scientist traveling to Vienna
aboard British Airways flight 700 on December 11, 2003, for suggesting the
billiard ball illustration in Chapter 11. All I know about him is that he was fiftytwo,
gray-haired, English-born, wrote poetry on yellow notepads, and was traveling
with seven suitcases since he was moving in with his thirty-five-year-old
Viennese girlfriend.
3 0 4 ACKNOWLEDGMENTS
animal. I was also flattered that Daniel Menaker took the time to edit my
text. I also thank Janet Wygal and Steven Meyers. The staff at Random
House was accommodating—but they never got used to my phone pranks
(like my trying to pass for Bernard-Henri Levy). One of the highlights of
my writing career was a long lunch with William Goodlad, my editor at
Penguin, and Stefan McGrath, the managing director of the group. I suddenly
realized that I could not separate the storyteller in me from the scientific
thinker; as a matter of fact, the story came first to my mind, rather
than as an after-the-fact illustration of the concept.
Part Three of this book inspired my class lectures at the University of
Massachusetts at Amherst. I also thank my second home, the Courant Institute
of Mathematical Sciences of New York University, for allowing me
to lecture for three quarters of a decade.
It is unfortunate that one learns most from people one disagrees
with—something Montaigne encouraged half a millennium ago but is
rarely practiced. I discovered that it puts your arguments through robust
seasoning since you know that these people will identify the slightest
crack—and you get information about the limits of their theories as well
as the weaknesses of your own. I tried to be more graceful with my detractors
than with my friends—particularly those who were (and stayed) civilized.
So, over my career, I learned a few tricks from a series of public
debates, correspondence, and discussions with Robert C. Merton, Steve
Ross, Myron Scholes, Philippe Jorion, and dozens of others (though, aside
from Elie Ayache's critique, the last time I heard something remotely new
against my ideas was in 1994). These debates were valuable since I was
looking for the extent of the counterarguments to my Black Swan idea and
trying to figure out how my detractors think—or what they did not think
about. Over the years I have ended up reading more material from those I
disagree with than from those whose opinion I share—I read more
Samuelson than Hayek, more Merton (the younger) than Merton (the
elder), more Hegel than Montaigne, and more Descartes than Sextus. It is
the duty of every author to represent the ideas of his adversaries as faithfully
as possible.
My greatest accomplishment in life is to have managed to befriend
people, such as Elie Ayache and Jim Gatheral, in spite of some intellectual
disagreements.
Most of this book was written during a peripatetic period when I freed
myself of (almost) all business obligations, routines, and pressures, and
went on meditative urban walks in a variety of cities where I gave a series
A C K N O W L E D G M E N T S 3 0 5
of lectures on the Black Swan idea.* I wrote it largely in cafés—my preference
has always been for dilapidated (but elegant) cafés in regular neighborhoods,
as unpolluted with persons of commerce as possible. I also
spent much time in Heathrow Terminal 4, absorbed in my writing to the
point that I forgot about my allergy to the presence of strained businessmen
around me.
* It is impossible to go very deep into an idea when you run a business, no matter
the number of hours the occupation entails—simply put, unless you are insensitive,
the worries and feelings of responsibility occupy precious cognitive space. You
may be able to study, meditate, and write if you are an employee, but not when you
own a business—unless you are of an irresponsible nature. I thank my partner, Mark
Spitznagel, for allowing me—thanks to the clarity of his mind and his highly systematic,
highly disciplined, and well engineered approach—to gain exposure to highimpact
rare events without my having to get directly involved in business activities.
GLOSSARY
Academic libertarian: someone (like myself) who considers that knowledge
is subjected to strict rules but not institutional authority, as the interest
of organized knowledge is self-perpetuation, not necessarily
truth (as with governments). Academia can suffer from an acute expert
problem (q.v.), producing cosmetic but fake knowledge, particularly in
narrative disciplines (q.v.), and can be a main source of Black Swans.
Apelles-style strategy: A strategy of seeking gains by collecting positive accidents
from maximizing exposure to "good Black Swans."
Barbell strategy: a method that consists of taking both a defensive attitude
and an excessively aggressive one at the same time, by protecting assets
from all sources of uncertainty while allocating a small portion for
high-risk strategies.
Bildungsphilister: a philistine with cosmetic, nongenuine culture. Nietzsche
used this term to refer to the dogma-prone newspaper reader and
opera lover with cosmetic exposure to culture and shallow depth. I extend
it to the buzzword-using researcher in nonexperimental fields
who lacks in imagination, curiosity, erudition, and culture and is
closely centered on his ideas, on his "discipline." This prevents him
from seeing the conflicts between his ideas and the texture of the
world.
Black Swan blindness: the underestimation of the role of the Black Swan,
and occasional overestimation of a specific one.
3 0 8 GLOSSARY
Black Swan ethical problem: Owing to the nonrepeatable aspect of the
Black Swan, there is an asymmetry between the rewards of those who
prevent and those who cure.
Confirmation error (or Platonic confirmation): You look for instances that
confirm your beliefs, your construction (or model)—and find them.
Empty-suit problem (or "expert problem"): Some professionals have no
differential abilities from the rest of the population, but for some reason,
and against their empirical records, are believed to be experts:
clinical psychologists, academic economists, risk "experts," statisticians,
political analysts, financial "experts," military analysts, CEOs,
et cetera. They dress up their expertise in beautiful language, jargon,
mathematics, and often wear expensive suits.
Epilogism: A theory-free method of looking at history by accumulating
facts with minimal generalization and being conscious of the side effects
of making causal claims.
Epistemic arrogance: Measure the difference between what someone actually
knows and how much he thinks he knows. An excess will imply
arrogance, a deficit humility. An epistemocrat is someone of epistemic
humility, who holds his own knowledge in greatest suspicion.
Epistemic opacity: Randomness is the result of incomplete information at
some layer. It is functionally indistinguishable from "true" or "physical"
randomness.
Extremistan: the province where the total can be conceivably impacted by
a single observation.
Fallacy of silent evidence: Looking at history, we do not see the full story,
only the rosier parts of the process.
Fooled by randomness: the general confusion between luck and determinism,
which leads to a variety of superstitions with practical consequences,
such as the belief that higher earnings in some professions are
generated by skills when there is a significant component of luck in
them.
Future blindness: our natural inability to take into account the properties
of the future—like autism, which prevents one from taking into account
the existence of the minds of others.
Locke's madman: someone who makes impeccable and rigorous reasoning
from faulty premises-—such as Paul Samuelson, Robert Merton the
minor, and Gerard Debreu—thus producing phony models of uncertainty
that make us vulnerable to Black Swans.
Lottery-ticket fallacy: the naive analogy equating an investment in collectGLOSSARY
3 0 9
ing positive Black Swans to the accumulation of lottery tickets. Lottery
tickets are not scalable.
Ludic fallacy (or uncertainty of the nerd): the manifestation of the Platonic
fallacy in the study of uncertainty; basing studies of chance on the narrow
world of games and dice. A-Platonic randomness has an additional
layer of uncertainty concerning the rules of the game in real life.
The bell curve (Gaussian), or GIF (Great Intellectual Fraud), is the application
of the ludic fallacy to randomness.
Mandelbrotian Gray Swan: Black Swans that we can somewhat take into
account—earthquakes, blockbuster books, stock market crashes—but
for which it is not possible to completely figure out the properties and
produce precise calculations.
Mediocristan: the province dominated by the mediocre, with few extreme
successes or failures. No single observation can meaningfully affect the
aggregate. The bell curve is grounded in Mediocristan. There is a qualitative
difference between Gaussians and scalable laws, much like gas
and water.
Narrative discipline: the discipline that consists in fitting a convincing and
well-sounding story to the past. Opposed to experimental discipline.
Narrative fallacy: our need to fit a story or pattern to a series of connected
or disconnected facts. The statistical application is data mining.
Nerd knowledge: the belief that what cannot be Platonized and studied
does not exist at all, or is not worth considering. There even exists a
form of skepticism practiced by the nerd.
Platonic fold: the place where our Platonic representation enters into contact
with reality and you can see the side effects of models.
Platonicity: the focus on those pure, well-defined, and easily discernible
objects like triangles, or more social notions like friendship or love, at the
cost of ignoring those objects of seemingly messier and less tractable
structures.
Probability distribution: the model used to calculate the odds of different
events, how they are "distributed." When we say that an event is distributed
according to the bell curve, we mean that the Gaussian bell
curve can help provide probabilities of various occurrences.
Problem of induction: the logical-philosophical extension of the Black
Swan problem.
Randomness as incomplete information: simply, what I cannot guess is
random because my knowledge about the causes is incomplete, not
necessarily because the process has truly unpredictable properties.
3 1 0 GLOSSARY
Retrospective distortion: examining past events without adjusting for the
forward passage of time. It leads to the illusion of posterior predictability.
Reverse-engineering problem: It is easier to predict how an ice cube would
melt into a puddle than, looking at a puddle, to guess the shape of the
ice cube that may have caused it. This "inverse problem" makes narrative
disciplines and accounts (such as histories) suspicious.
Round-trip fallacy: the confusion of absence of evidence of Black Swans
(or something else) for evidence of absence of Black Swans (or something
else). It affects statisticians and other people who have lost part
of their reasoning by solving too many equations.
Scandal of prediction: the poor prediction record in some forecasting
entities (particularly narrative disciplines) mixed with verbose commentary
and a lack of awareness of their own dire past record.
Scorn of the abstract: favoring contextualized thinking over more abstract,
though more relevant, matters. "The death of one child is a
tragedy; the death of a million is a statistic."
Statistical regress argument (or the problem of the circularity of statistics):
We need data to discover a probability distribution. How do we know
if we have enough? From the probability distribution. If it is a Gaussian,
then a few points of data will suffice. How do we know it is a
Gaussian? From the data. So we need the data to tell us what probability
distribution to assume, and we need a probability distribution to
tell us how much data we need. This causes a severe regress argument,
which is somewhat shamelessly circumvented by resorting to the
Gaussian and its kin.
Uncertainty of the deluded: people who tunnel on sources of uncertainty
by producing precise sources like the great uncertainty principle, or
similar, less consequential matters, to real life; worrying about subatomic
