The Red Swan

Cover RG Wallace 'Red Swan' Full Version PDFThe following is an excerpt from our first e-single, available here as a PDF. Consider the single, on the political economy of Nassim Taleb of ‘Black Swan’ fame, a freebie. We do ask that those who can afford it consider donating to Farming Pathogens. Your help is greatly appreciated. The full version adds explorations of Taleb’s animosity towards science, his anti-theoretical theory of history, his assumptions about human nature, and, for good and for ill, applications of Black Swan thinking to disease modeling.

Perhaps by chance alone Nassim Nicholas Taleb’s best-selling The Black Swan: The Impact of the Highly Improbable, followed now by the just released Antifragile, captures the zeitgeist of 9/11 and the foreclosure collapse: If something of a paradox, bad things unexpectedly happen routinely.

For better and for worse, Black Swan caustically critiques academic economics, which serve, more I must admit in my view than Taleb’s, as capitalist rationalization rather than as a science of discovery.

Taleb crushes mainstream quantitative finance, but fails as spectacularly on a number of accounts. To the powerful’s advantage, at one and the same time he mathematicizes Francis Fukuyama’s end of history and claims epistemological impossibilities where others, who have been systemically marginalized, predicted precisely to radio silence.

Power, after all, is the capacity to avoid addressing a counternarrative.


A ‘Black Swan’ is an unexpected event of great impact that many an observer rationalizes after the fact. While related, Taleb’s swan differs from Karl Popper’s. Popper proposed the search for a black swan as the proper means of testing the proposition that all swans are white. Falsification offered a work-around for the problem of induction, whereby we mistakenly generalize conclusions on the basis of a few observations.

Taleb is more concerned with the reasons for, and consequences of, the difficulties academics and financial analysts have in assimilating unexpected events into their models. According to Taleb, many researchers confuse the frequency of events with their likely effect. In tweed or pinstripe, they repeatedly confound low frequency and low impact. As a result, anomalies are ignored.

Practitioners transform the fallacy back into a mathematical given. The Gaussian measures of risk most researchers use exclude Black Swans as out beyond the distributions they assume beforehand.

Taleb, for instance, sticks it to Robert Merton, Jr., Nobel laureate, father of learning portfolio theory, and Long-Term Capital Management founding partner, whose Gaussian risk models, Taleb says, ruled out large deviations, leading LTCM to take on the monstrous risk that sank the firm. Models—however elegant their formalism—rarely fit reality when built on false premises.

The details are worth exploration.

Under the Gaussian (or normal or bell curve) distribution, the arithmetic mean stabilizes as the population increases. Most of the population is distributed about the mean, with only a small fraction found in the extreme tails. As we can effectively ignore these infrequent ‘outliers’, the population becomes characterized by a particular bound of known dispersion.

Take a ‘population’ of coin flips. The Gaussian emerges by two effects Taleb shines in explaining. First, if the outcomes—heads or tails—have an equal and, on each flip, independent chance, it would be highly unlikely we would end up with many of the same kind in a row the more flips we make. The unlikelihood explains why the tails of the distribution are so small, and why these extreme deviations precipitously decline in frequency the more flips we add. What, after all, are the chances we hit 32 heads in a row? Or 320?

In the second effect, the various combinations by which half heads/half tails can be produced increase the frequencies for the more mixed outcomes. The combinatorial explains why the frequencies around the mean are so large. There are a lot of ways of producing half heads/half tails: for four flips, for instance, HHTT, HTHT, TTHH, THTH, HTTH, and THHT. For forty flips, many, many more.

The Gaussian arbitrarily sets the “standard” deviation, the range -1s to 1s straddling the mean, as containing 68.27% of the population. The more standard deviations added, that is, the more we move away from the hump of the curve toward the tails, the more exponentially the number of observations added declines. The second and third deviations, for instance, hold 95.45% and 99.73% of values, respectively. The sharp drop-off emphasizes how much the observations are concentrated about the mean and the great unlikelihood of outliers or, at the most extreme, Black Swans.

Populations differ in their specifics, of course. Each’s Gaussian curve is defined by the equationRed Swan equation, with a the curve’s peak, b its position along the x-axis, c the width of the curve, and e Euler’s number. The curve’s characteristic kurtosis and skew are dependent in part on the population’s inherent variation and, if constructed by sampling, the size of the sample taken.


There are a number of ironies in Taleb’s treatment of the bell curve. He identifies an essentialism in the Gaussian view, which treats what it views as the utter unlikelihood of Black Swans as something real. The thinking of the biometricians behind the modern statistical derivation of the curve was in fact in direct opposition. As Ernst Mayer describes it, Darwinism switched biologists out of an essentialist thinking, which saw the mean form as a real archetype and deviations thereof as counterfeit, to viewing reality in the variation of a population and the construct in the mean.

Without explanation Taleb says he accepts the application Darwin’s half-cousin Francis Galton and colleagues made of the Gaussian to genetics and heredity, probably, if we must attach a reason, because biological measurements often approximate the distribution. Taleb, however, sees in its application to social systems a sham. Human societies are inherently uncertain, he says, free of the law of large numbers, which underlies the Gaussian. On what grounds he frames biological systems as tidier than their social counterparts is unclear. Biological systems are routinely lurching through regime shifts that stretch out and pop normal distributions.

At the same time, his assertion about human societies fails inspection. By the very statistical physics Taleb claims can circumvent Gaussian gaffes, Rodrick Wallace and Robert Fullilove show regression models explain violence and other risk behaviors at multiple geographic scales across the U.S. Wallace and Fullilove conclude racial and economic apartheids stateside constrain behavioral dynamics across population and place.

In other words, social systems can impose the kind of structure that turns populations Gaussian in nature, even through the country’s various demographic shifts, under some conditions back to the founding of the republic. Manhattan’s Lower East Side, for instance, has been home to impoverished populations of black slaves, immigrant Jews, and, now, Loisaida.

In a third irony, Taleb sets the social origins of Gaussian statistics in the aspirations of the 18th century European middle class, a sheeple, in Taleb’s characterization, that bet on a future of mediocre living against its fear of divergent outcomes. He attaches Saint-Simon, Proudon and Marx to the political hope of a statistical aurea mediocritas. He spins Marx, the revolutionary punctuated equilibrist, into a straw man who champions at one and the same time the fallacy of the average man—average in everything he does—and the glorification of mediocrity found in la loit des erreurs, wherein even the standard deviation was thought more error than natural variation.

“No wonder Marx fell for Adolphe Quetelet’s ideas,” Taleb concludes QED. As if industrial countries with the highest Gini scores don’t also suffer some of the worst indices in every social and health category, affecting, if by dint of spatial contagion alone, rich and poor alike. As if rich people are by definition also brilliant, etc., a recapitulation of the fallacy of the average man in reverse. As if copious wealth doesn’t also select for sloppy thinking, Taleb’s own complaint elsewhere in the book.


The Mandelbrotian or fractal, in contrast, rejects the notion of a quantifiable dispersion of known and ‘standard’ deviations on which Gaussian statistics, including correlation and regression, depend. Even the latter’s notion of statistical significance is, to Taleb, reified. How can a sample be considered ‘significant’ when compared to a distribution that isn’t real?

Benoît Mandelbrot identified the fractal—repeating patterns across scales—as the geometry of the Black Swan. While Gaussian probabilities collapse toward the tails, fractals (somewhat) preserve probabilities across scales—even toward the tails—better conserving the possibility of extreme events. In other words, the fractal is, unlike the Gaussian, invariant to scale.

Taleb claims the fractal as how nature works, as Platonic a notion as the geometry he condemns. Yes, snails, leaves, snowflakes, shorelines, lightening, and peacocks, among many examples, exhibit fractal patterns, but not all of nature need fold in on itself in this way. Scale effects abound. As ecologist Simon Levin describes, some characteristics are specific to one scale and not others. Taleb concedes fractality has its limits. He also concedes we are unable to say where to draw the line for any one fractal:

Even as we can scale the fractal with non-ordinal exponents, say, 1.5 or 3.2, the fractal isn’t something we observe, but something we can only guess or infer from the data we collect. In other words, despite Taleb’s efforts to naturalize fractals—and by extension Black Swans—they are as ideational as Gaussian ‘mediocrity’. It isn’t that we can predict Black Swans, fractal or no, as by Taleb’s tautology, if we can predict it, it isn’t a Black Swan, but, Taleb continues, that we should acknowledge they exist and we should budget or bet accordingly.

There have long existed alternatives apparently off Taleb’s radar, however. We could ask, for instance, if he’s such an empiricist, why not let the data he repeatedly refers to speak for themselves? Markov-chain Monte Carlo analyses of millions of trials can approximate the distribution under which the system as a whole is generated and against which we can contrast our sample set, including for so-called Grey Swan systems we might actually be able to predict. Indeed, there are nonparametric analogs to ANOVA, regression and correlation: Kruskal-Wallis, ANOSIM, kernel regression, Spearman’s rank correlation, etc.

The Popperian nulls Taleb champions are in the meantime increasingly abandoned for a Bayesian structure, whereby probabilities are assigned (and reassigned with each new datum) to a series of hypotheses.

Even Taleb’s central dichotomy smells. From the Wallace and Fullilove example alone, we can see a regression structure operating at multiple scales. A fractal series of Gaussian distributions.


Our objections to Talebs’ treatment needn’t be confined to technicalities. If we follow Taleb’s lead and historicize his own line of thought we discover a particular political logic.

Taleb, channeling Allen Ginsberg’s Moloch, appears to exist in an acosmos in which his metaphysics are affirmed only by the money he can make off it. He says he came to abandon the notion we can discover the market’s laws of history. He knows only that bad things happen regularly, if rarely, and with devastating impact. Half of the market’s earnings over the past fifty years accrued across ten separate days of trading. So over the long haul, Taleb shorts the market even if he doesn’t know the reasons why it intermittently (and catastrophically) collapses.

He does identify brokers’ premise of a steady rate of return as one such self-fulfilling cause, producing events that happen precisely because they weren’t expected. Conversely, he claims, what we already know doesn’t happen because we make ready for it.

But Taleb makes a mash of the political economy of knowledge. For we need ask, who knows and who acts on that knowledge? At my end of the pool, in epidemiology, many practitioners know, for one, that turning poultry and livestock into monocultural widgets helps produce deadly epizootics, a conclusion suppressed here in the United States of Agribusiness with Lysynkoist ferocity.

Because treating the market as a black box has paid off for him, Taleb, putting his money where his brain is, characterizes reality for all practices and purposes as random. But surely just because something doesn’t go according to plan doesn’t mean no cause exists. This Taleb acknowledges, but defines the failure of prediction—of appropriating information—as an estemic opacity, that is, as equivalent to physical randomness.

Taleb derides utopianists who fail to assimilate such ambiguity and by a Plantonic fallacy confuse the narrative map for the territory,

So I disagree with the followers of Marx and those of Adam Smith: the reason free markets work is because they allow people to be lucky, thanks to aggressive trial and error, not by giving rewards or ‘“incentives” for skill. The strategy is, then, to tinker as much as possible and try to collect as many Black Swan opportunities as you can.

But can we conclude his own treatment here as doing otherwise? With every commercial on TV, and every business book, capitalists immanentize the eschaton, promising transcendental fulfillment with every bar of soap and financial model sold.

We need ask again, free markets are free (and generously trial and error) for whom? Capital parlays stealing the majority’s degrees of freedom—its capacity to organize the means of production on its own terms—into wealth for a few. Everyone else without capital pays the price. On a $1 a day, there is little room for trial and error without the severest consequences. These people don’t exist here, however. Throughout his books Taleb repeatedly shows himself unable to think outside his own class, which includes the academic enemies against whom he rails. I find this telling.

There is too the inconvenience that the market has little do with innovation. Doug Henwood describes initial public offerings, ostensibly initiated to raise the funds companies need to grow, raise little, if any, capital. The largest firms, which regularly retire hundreds of billions of dollars more in stock than they issue, finance research and production by way of in-house funding streams. Stock is instead a means by which the wealthy negotiate ownership, and attendant claims on societal power, among themselves.

In that case, then, Taleb’s conclusion about trial and error resonates for all the wrong reasons, “I then realized that the great strength of the free-market system is the fact that company executives don’t need to know what’s going on,” as much a rationale for incompetence as indemnifying executives of the responsibilities of an economic Maxwell’s demon who tracks every transition.

Flippant stochasticity ‘works’ well if there exist mechanisms for self-correction. Almost all such corrections, however, are presently externalized. Consumer, worker, nature, governments—always someone else—must pick up the cost of rentier bad judgment or willful malfeasance. The ‘freer’ economies are—that is, the more deregulated—the more executives should know what they are doing, from the prole viewpoint anyway. Otherwise, contrary to Antifragile’s core argument, the greater the impact of executive failures the larger society suffers.


Taleb identifies a biological source of our innumeracy,

We do not spontaneously learn that we don’t learn that we don’t learn. The problem lies in the structure of our minds: we don’t learn rules, just facts, and only facts. Metarules (such as the rule that we have tendency to not learn rules) we don’t seem to be good at getting. We scorn the abstract; we scorn it with passion.

Perhaps metarules aren’t rules either, however. Indeed, Taleb’s complaint appears directed at a particular Anglo-American cultural moment, integral to the kind of technocist capitalism Taleb embraces.

We know rare events aren’t synonymous with uncertainty. There are any number of astronomical events we can predict: comets, simultaneous planetary transits, reversals in Earth’s axial tilt, etc. In the other direction, randomness can happen at many temporal scales, including, when continuous, as stochastic noise. What Taleb is trying to get at here, however, is that rare and random events surprise us worst, if particularly because they are camouflaged by the workaday. We can’t, or refuse to, get our minds wrapped around that failure.

Taleb sees in the Gaussian approaches an attempt to quantify what is in actuality is unknowable risk. Such efforts typically suffer the ludic fallacy, whereby the odds of an event are defined by games of chance with known denominators. We know, for instance, that any side of a fair die has 1/6 a chance upon a throw. Can we really prescribe risk for something much more complex—for which we can’t describe—such as a pandemic or collapse in the housing market?

In this way, Taleb repeatedly positions himself as a slayer among Gaussian dragons. His braggadocio appeals to this transplanted New Yorker of childhood heroes Giorgio Chinaglia and Reggie Jackson, but whatever their pose and style, scientists, like athletes, are, as Joseph Campbell quotes Oswald Sprengler, integral parts of their historical moments,

“Supposing…that Napoleon himself, as ‘empirical person’ had fallen at Marengo—then that which he signified would have been actualized in some other form.” The hero [Campbell continues], who in this sense and to his degree has become depersonalized, incarnates, during the period of his epochal action, the dynamism of the culture process…And insofar as the hero’s act coincides with that for which his society is ready, he seems to ride on the great rhythm of the historical process.

Where does Taleb’s ride take him? He diagnoses a triplet of opacities predictions suffer. Many, perhaps Campbell himself, fill in what history refuses to divulge, producing an illusion of understanding, in which specific events stand in for historical circumstance. Or they produce a retrospective distortion that imports wishful revisionism. Or an overvaluation of factual interaction, from which grand schema are inflated puff by Platonic puff.

Taleb’s ‘novel’ preoccupation with revolutionary outcomes, abandoning essentialist quasi-equilibria, is dialecticism’s old hat. And yet it’s also the latter’s diametric opposition, for Taleb has turned humanity’s struggle with itself into no history at all. In Taleb’s world, regimes—economic and otherwise—aren’t overturn by due cause but by chance alone.

By virtue of excising causality—and blame and responsibility—Taleb, even as he assures us he wishes he wouldn’t have to, reframes the nature of the world in an essentialist stochasticity. The world is beyond our capacity to act on it. Despite rejecting determinism, if only as something we can act on, Taleb channels his Wall Street colleagues’ contempt. The world matters only as it is filtered through the market, which, like God, is both necessary and unfathomable. And everybody else must act as a means to its ends.

The key point here is that the Black Swan isn’t merely a statistical phenomenon. It is an idea that can be bent to serve its masters.


Taleb took his doctorate in derivatives, but ended up betting against them as they precipitate negative Black Swans whose mathematical errors compound losses. At first, Taleb traded against the instruments’ technical inefficiencies—one instrument against another—before abandoning the horse race approach for a more insurance-like stance against the entire class of models, along the lines of the financial freaks of Michael Lewis’s sideshow.

The October 1987 market collapse left Taleb a very rich man, with enough fuck-you money to quit the trading floor but remain in the quant world of data that he says “thinkers” can’t see. He became a cafe flâneur, a self-styled limousine philosopher who, in his middlebrow way, could both bash middlebrow academics and intellectualize greed. The latter emerges as an entelechy, rather than—with 662 American bases in countries around the world—by primitive accumulation.

“There is more money,” Taleb echoes William Gibson’s Hubertus Bigend,

in designing a shoe than in actually making it: Nike, Dell and Boeing can get paid for just thinking, organizing, and leveraging their know-how and ideas while subcontracted factories in developing countries do the grunt work and engineers in cultured and mathematical states do the noncreative technical grind. The American economy has leveraged itself heavily on the idea generation, which explains why losing manufacturing jobs can be coupled with a rising standard of living.

Whatever we may say of Taleb, he is efficient, packing in many an absurdity in so few lines.

It isn’t intellectual property that’s parlayed into capital, for one. In 2005, for instance, industrial designer Dan Brown patented a new wrench whose prongs encircle a screw like a camera shutter. Sears, which first sold Brown’s wrench, offshored the design Walmart-style to a Chinese manufacturer, and now, daring Brown to sue, sells the knockoff under the Craftsman brand at a more competitive price. “I’m in favor of free trade,” Brown recently told the New York Times, “The person who’s out-innovated loses.” What Brown misses is that the theft, not the patent, is now the intellectual innovation.

Brown isn’t an anomaly. His expropriation is emblematic of a systemic deformity. As Giovanni Arrighi explains it, capitalism entered one long if shifting crisis in the early 1970s. For the first decade intensive competition induced falling rates of profit. Organized labor could still at this point put up a good fight against capital’s attempts to shift such losses onto workers via productivity gains and other givebacks. In the Anglo-American sphere, Margaret Thatcher and Ronald Reagan broke labor’s national reach, with the aim of depressing wages and benefits.

A capitalism now less bound by such annoying overhead as labor rights and environmental standards, Arrighi continues, switched into an overproduction crisis. When income is concentrated into the hands of the few, effective demand collapses.

This second crisis was mitigated—and ultimately exacerbated—two ways. Finance’s not-so-fictitious speculation stumbled from bubble to bubble, spreading surplus capital and producing booms—and inequality—that covered up the economy’s underlying ill-health. Demand meanwhile was itself turned into a market for new financial instruments. Workers were extended comical lines of credit, their debts themselves speculated on, a bubble popped by the housing collapse, severely degrading the economy and leaving millions penurious.

Keynesian intervention—for anyone other than the biggest banks—was viewed by an albeit divided capital class as too much a political risk. It would open the door to reversing labor’s fortunes. In other words, at least until the Occupy movement took off, the kleptocrats were perfectly comfortable with, and some maniacal about, a pauperized population. Better to rule a banana republic of ‘right-to-work’ than share what remains of a declining empire.

David Harvey describes how capital spatially parlayed its structural risk. Reintegrating the Soviet bloc into circuits of capital; the economic liberalization of China (and just about every other country); interlinking the world’s financial markets; and innovations in transportation and communication, including containerization, eased capital flows, extended lines of production and distribution, and press-ganged millions more into the global industrial reserve army. Once such conditions are in place, the globe becomes a proverbial toy,

Why invest in low-profit production when you can borrow in Japan at a zero rate of interest and invest in London at 7 per cent while hedging your bets on a possible deleterious shift in the yen-sterling exchange rate?

The more capital surplus produced as a result, however, and the larger the extent across which it is produced, the greater (and faster) the reinvestment required, the fewer the relative opportunities to do so, and the greater the risks must be taken to somehow somewhere recapitalize—privatizing fire departments, marketing credit cards to prepubescents—as a result increasing the precariousness of the entire apparatus.

The rot, then, isn’t found merely in the schemes of desk scalpers such as Nicholas Leeson and Kareem Serageldin covering up bad bets, in the likes of higher-ups Jeffrey Skilling and Jon Corzine, or even in the infrastructural corruption of Libor and Timothy Geithner’s New York Federal Reserve. The system is the rot.


Taleb argues humanity is moving increasingly into a world defined by Black Swans rather than by centroidal gravity. Winner-takes-all tournaments in politics and economics, yes, but in the ‘harder’ version he omits, a socialism for the rich. Cumulative advantages—whether it be in finance or in academic reputation—are politically protected. Those without such initial capital drop out. Precocity or genius matters little. Social resources, whether or not won by merit, do. Conversely, those who lose continue to mount losses in a ratchet downwards.

So the dynamics of inequality feed on their own momentum. Any Marxist could tell you that. But despite all the evidence to the contrary, the details available even in more mainstream outlets than Arrighi and Harvey, Taleb rejects it as an outcome of the system itself. After all,

one had only to look around to see that these large corporate monsters dropped like flies. Take a cross section of dominant corporations at any particular time; many of them will be out of business a few decades later, while firms nobody ever heard of will have popped onto the scene from some garage in California or from some college dorm…[A]lmost all [the] large corporations were located in the most capitalist country on earth, the United States. The more socialist a country’s orientation, the easier it was for the large [failing] corporate monsters to stick around.

Taleb transubstantiates luck into an equalitarianism that destroys even the largest company in favor of the smallest “little guys”. A system structured around the most vicious exploitation, with Gini scores in the stratosphere, is now the most equalitarian. It’s the legend of Microsoft and Facebook—frogs kissed by Lady Luck into princes.

But the system remains, whatever the turnover. Capital and governmental subsidies are rolled over from one technological regime to the next. Exxon, BP and GE, paying no taxes, have a stranglehold on the political economy, whatever Valdez or Gulf spill may come. Diseconomies of scale, inherent to capital accumulation, are politically protected. Cumulative advantage is a class prerogative continually financed by expropriating labor, who, in Taleb’s world, don’t even qualify as the “little guys” to whom he repeatedly alludes.

In other words, Taleb suffers his own case of epistemic opacity, imparting to chance well-documented processes of which he knows nothing or to which he turns a blind eye.

To Taleb, capitalism’s problems emerge by stupid thinking or by chance. True enough on both accounts, but there is as well primitive accumulation, corruption, political expediency, and intrinsic structural contradictions, the costs of which are externalized to workers, consumers, governments and the environment. It’s always someone else who picks up the bill, permitting bad economics to masquerade as bad luck, off of which Taleb himself wins big betting against. From this vantage, Taleb has a vested interest in letting systemic failure off the hook.


Willful ignorance of the market’s historical context—after all we can’t track history—colors more than Taleb’s statistical, and by extension political, assumptions. His behavioral proclivities are nigh on pronoid. Taleb, adding insult to injury, writes in parable of a regular “compassionate” prank. He’d give a taxi driver a $100 bill as a tip,

I’d watch him unfold the bill and look at it with some degree of consternation ($1 million certainly would have been better but it was not within my means). It was also a simple hedonistic experiment: it felt elevating to make someone’s day with the trifle of $100.

As if his ilk hadn’t already structurally punked the immigrant into a hemorrhoid driving sixteen hours a day. I’m sure the driver appreciated the fare, but the self-aggrandizement—at the heart of every $10,000-tip-for-the-waitress story—speaks to a mélange of guilt, fear and contempt. Tithes to the gods of fate.

Tellingly Taleb ends the tips, “We all become stingy and calculating when our wealth grows and we start taking money seriously.” We do, do we? Even such ineffectual redistribution, a contemptuous tease, becomes anathema the greater the inequality. For those increasingly in the know about how utterly preposterous their prosperity, tithing apparently only alerts angry gods where to strike.

To his credit, Taleb destroys conservative ideologues, who are none too conservative, “just phenomenally skilled at self-deception by burying the possibility of a large, devastating loss under the rug.” On the other hand, one can’t help but think them truly conservative when the whole system is dedicated to protecting them against losses, “[W]hen ‘conservative’ bankers make profits, they get the benefits; when they are hurt, we pay the costs,” producing, as I’ve described elsewhere, moral hazards of apocalyptic proportions.

Indeed, the whole notion of compensation is out-of-whack, even within the confines of a capitalist economy dedicated to theft. Bankers are paid annual bonuses for short-term profits they lose once a Black Swan hits,

[T]he tragedy of capitalism is that since the quality of the returns is not observable from past data, owners of companies, namely shareholders, can be taken for a ride by the managers who show returns and cosmetic profitability but in fact might be taking hidden risks.

Of course, while Taleb’s point is worth salvaging—capitalism incentivizes cons—the rest of us, the poisoned and dispossessed, the billions who literally don’t know how they are to survive the month, can only snigger low and slow at Taleb’s view of ‘tragedy’.

Is it any wonder Taleb and Big Tobacco shill and fellow New York Times bestselling author Malcolm Gladwell profile each other? Each stakes the claim our social problems are nothing of the sort and are in actuality mathematical perversions dumb innumerates can’t see. Gladwell’s classic prison guard solution—fire the few abusive guards—is a neoliberal apologetics for a system that by percent imprisons five times more blacks than the greater population. Abusing the poorest is that system’s natural order, and prison its rationalization, with enough ‘bad-apple’ deniability to indemnify itself. Gladwell’s pragmatic technocrat, aiming to run the police state more efficiently, is an ideologue by another name.

Even the most thoughtful of allies will find it hard to blind themselves to the breadth of Taleb’s myopia. He misses that the money he makes off shorting these conservatives—his second-order gains—is also folded into the system’s protection. The loot begs whether organized opposition of any seriousness, inclusive of waitresses and hemorrhoidal cab drivers, their Swans spotted with blood, would bother to parse the difference.


Is there, then, an alternative? How would a Red Swan that assimilates chance’s political context change our perspective (and our capacity for action)?

By all appearances dialectical biologists Richard Levins and Richard Lewontin, who for five decades have applied their approaches to studying biological systems, take Taleb head on,

Randomness has been associated with lack of causality, and with unpredictability and thus of irrationality, a lack of purpose, and the existence of free will. It has been invoked as the negation of lawfulness and therefore of any scientific understanding of society. It then becomes a justification for a reactionary passivity. As the bumper sticker says, “Shit happens.” So stop complaining. For the most part, however, randomness and causation, chance and necessity, are not mutually exclusive opposites but interpenetrate.

A car crash, for instance, involves two drivers whose trips were determinate and even planned. The crash is ‘random’ only as the two cars’ trajectories were independent. So contra Taleb, the quantum notion of randomness isn’t synonymous with causal independence. The latter point is particularly acute for mesoscale, heterogeneous systems, such as ecosystems and societies, which Levins and Lewontin describe as characterized by, “a very large number of individually weak forces…essentially independent,” with respect to each other.

Randomness, then, should always be defined in terms of its scale or to other objects. In Levins and Lewontin’s example, Franklin Roosevelt’s death was no accident as to the state of his body but random as to the international politics of his day.

Determinacy, meanwhile, can arise out of randomness. All the molecules of a chair need not shift together—causing the chair to jump in Taleb’s example—for the sum total to produce Newtonian objects. If we can’t predict every mutation, we can still infer exposing organisms to radiation and toxic chemicals will produce more mutations.

Levins and Lewontin offer a third example. Months before the Chernobyl accident, the plant’s director assured an interviewer only 1-in-10,000-years odds of an accident. Sounds crazy, given what followed. But at the level of Europe’s 1000 reactors, an accident at those odds should happen once every ten years. “A chance event with low probability,” the dialectical duo write, “becomes a determinate certainty when there are a large number of opportunities.”

Causality can be found in the aggregate. And the Black Swan can turn deterministic.

Conversely, Levins and Lewontin continue, randomness can arise out of determinacy. Computers, in their example, can generate random numbers. But these are more accurately pseudo-random as their generative rule is deterministic (and their sequence repeatable). But they are random in relation to the simulation for which one is using them.

Finally, random processes are bounded. Not everything goes. Randomness in real life is constrained by states of origin. In contradiction to Taleb’s sweeping pronouncements, boundaries as they apply to social processes are the focus of fruitful research. So while humanity, and society more generally, is no machine—and here Levins and Lewontin strike at the core—“The error is to take the individual as causally prior to the whole and not to appreciate that the social has causal properties within which individual consciousness and action are framed.”

Indeed, one can apply their observation to The Black Swan itself,

While the consciousness of an individual is not determined by his or her class position but is influenced by idiosyncratic factors that appear as random, those random factors operate within a domain and with probabilities that are constrained and directed by social forces.

In other words, Taleb’s books stand as their own refutation.

The full version of the essay is available here.

7 Responses to “The Red Swan”

  1. Amazing critique. The author of Red Swan (Wallace) eloquently elaborates on every qualm that arose while I was reading Taleb’s books. Thank you for writing this!

  2. rupertread Says:

    A key point you miss is that Taleb, like Larry Lohman, criticises derivatives – and similarly EBM, Kyoto, pollution – for MULTIPLYING uncertainty and thus increasing not only tail-risk but also ignorance.

  3. A good point, rupertread, one I thought I acknowledged but perhaps not as clearly as I should have. I wonder, however, if Taleb’s treatments are second-order manifestations of his own complaint, if his contention the bond market can’t survive stimulus spending is any indication. A textbook neoliberalism that protects the system he banks on shorting.

  4. And thank you for the kind words, emergencekit.

  5. […] In contrast to Nassim Taleb’s Black Swan – history as shit happens – we have here an example of stochasticity’s impact arising out of deterministic agroeconomic policy – a phenomenon I’ve taken to calling the Red Swan. […]

  6. […] In contrast to Nassim Taleb’s Black Swan—history as shit happens—we have here an example of stochasticity’s impact arising out of deterministic agroeconomic policy—a phenomenon I’ve taken to calling the Red Swan. […]

  7. […] In contrast to Nassim Taleb’s Black Swan—history as shit happens—we have here an example of stochasticity’s impact arising out of deterministic agroeconomic policy—a phenomenon I’ve taken to calling the Red Swan. […]

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