Montreal, April 15, 2011 • No 288


Jean-Hugho Lapointe is a lawyer. He holds a certificate in business administration from Université Laval.



Economic Predictions, Central Planning
and the Pretence of Knowledge


by Jean-Hugho Lapointe


“An economist is an expert who will know tomorrow why the things he predicted yesterday didn’t happen today.


–Laurence J. Peter


          While most of us get the feeling that economic predictions are just as unreliable as weather forecasts, demand for economic predictions somehow remains very strong, as few people seem interested in their track record. Indeed, the business of predicting the future is as old as Antiquity, and has yet to significantly improve its flawed product, but still, people keep flocking to the altar.


          Nowadays, some segments of the prediction business, such as weather and economic predictions, are cunningly marketed as "scientific forecasting," as they make extensive use of mathematical models. But since I am quite certain that neither these models nor the predictions made using them could ever be considered sufficiently reliable to be used as evidence in a court of law to demonstrate knowledge of what the future holds, I have always been puzzled by the fact that mainstream economists, who should know better, use them to this very end. As a consequence of this, public policy remains guided by economic forecasters and their models, despite systematically humiliating results and terrible consequences for human lives and society.

          If astrologers and meteorologists have little influence on public policy, economists predicting the future occupy strategic positions throughout the public decision-making process, whether in government or at central banks, nurturing the enactment of more and more wrongheaded large-scale policies. That the work of economic forecasters continues to escape close scrutiny seems disturbing at the very least, and prompted the writing of this paper.

It’s the complexity, stupid!


“Complex: involving a lot of different but connected parts in a way that is difficult to understand.


–Cambridge Learner’s Dictionary


          As I pointed out, economic predictions are based on mathematical economic models. In science, a model is generally understood as an abstract representation of a given subject, such as a process or a system. Mathematical models are models which use mathematical language (data, equations) to describe their underlying systems, and are found in a variety of fields such as physics, economics, biology, meteorology and climatology, etc. Mathematical economic models are thus abstract representations of an economy based on available data and equations. One of the outputs of these models are quantitative predictions of the future state of the economy depending on what actions are taken at a given moment, allegedly similar to the way a model of the solar system can predict the future position of the planets given the correct present data.

          Since mainstream economics relies on these tools to formulate theories and prescriptions, mathematical economic models have become the foundation of most advice guiding public policy, such as the setting of interest rates, for some decades now. Indeed, once one assumes that we can predict the state of the economy, what follows is the idea that we can foresee the results of our actions, and therefore properly manage society. Yet, as Albert Einstein said as far back as the early 40s:

When the number of factors coming into play in a phenomenological complex is too large, scientific method in most cases fails us. One need only think of the weather, in which case prediction even for a few days ahead is impossible. Nevertheless no one doubts that we are confronted with a causal connection whose causal components are in the main known to us. Occurrences in this domain are beyond the reach of exact prediction because of the variety of factors in operation, not because of any lack of order in nature.(1)

          Since that time, complexity theory has gained momentum in a variety of scientific fields as a way of understanding complex systems, but it seems that economic forecasting remains impervious. We will discuss below what could become the next paradigm shift in economics.

          Einstein was a dazzling thinker on a number of subjects, among them the limits of science (or the philosophy of science). He was among the first to identify the peculiar obstacles posed to science by complexity, and on this he was followed by mathematician Warren Weaver, considered to be a pioneer on the subject. Then, inspired by Weaver’s work, the first to specifically discuss the scientific implications of complexity in economics was Friedrich Hayek. In The Theory of Complex Phenomena (1964), he pointed out that what distinguishes complex phenomena (such as the economy) from simpler phenomena is the multiplicity of elements and of their relationships within the system, coupled with the subjectivity of the data in social sciences that eludes mathematical formulae.

          Nevertheless, complexity theory seemed to gain real traction only in the 90s, as described by Cornell mathematician Steven Strogatz:

Every decade or so, a grandiose theory comes along, bearing an ominous-sounding C-name. In the 1960s it was cybernetics. In the '70s it was catastrophe theory. Then came chaos theory in the '80s, and complexity theory in the '90s. In each case, the skeptics at the time grumbled that these theories were being oversold and that the results were either wrong or obvious. Then everyone had a good laugh and went back to the lab bench for some more grinding, reductionist science, walled off from their colleagues in adjoining disciplines, who were themselves grinding away on their own tiny corners of the universe [...] What's different now is a feeling in the air. Even the most hard-boiled mainstream scientists are beginning to acknowledge that reductionism may not be powerful enough to solve all the great mysteries we're facing: cancer, consciousness, the origin of life, the resilience of the ecosystem, AIDS, global warming, the functioning of a cell, the ebb and flow of the economy [...] What makes all these unsolved problems so vexing is their decentralized, dynamic character, in which enormous numbers of components keep changing their state from moment to moment, looping back on one another in ways that can't be studied by examining any one part in isolation. In such cases the whole is surely not equal to the sum of the parts. These phenomena, like others in the universe, are fundamentally nonlinear.(2)

          One of the first significant scientific developments to come out of studies of complexity came in meteorology in the early 2000s. Another mathematician, David Orrell, sparked off a debate in this field when he put forward the idea that errors in weather forecasts were not attributable to chaos but rather to errors in the models. Further, he claimed that errors in the models are insurmountable because of the complexity of the underlying system; unlike chaos, complexity is incomputable.

          If his argument has made inroads in meteorology, mainstream economics (which uses similar models for similar systems) has mostly sailed over the entire issue so far. Obviously, there are immense political implications for mainstream economics that are not shared by meteorology and that might present an incentive for maintaining the status quo. This leads to a strange divergence in which, out of two mathematical models sharing similar physics, methods and limitations, one is known to not be reliable enough to decide on buying waterproof clothes for a trek planned in a week, whereas the other still settles public policy decisions designed to affect millions of lives.

          Orrell addresses the limitations of mathematical models with regard to predicting complex systems such as the economy, health or the climate in his book, The Future of Everything.(3) He first distinguishes between chaos and complexity. One of their main differences is that there is no order in chaos, whereas order emerges spontaneously in complex systems. And indeed, without any central will planning it, order exists in nature, in living organisms and in economies. It emerges out of the multiple relationships and their feedback effects between the various elements of the systems, as these feedbacks create an ever-adjusting balance. Another emerging property of some complex systems is adaptation.

          Such properties of complex systems are difficult for models to capture because they are alien to reductionism. Order and adaptation are displayed by a system as a whole and cannot be understood under the same rules that govern the relationships between the individual elements of the systems. Think of the human brain: drawing a map of all its neurons and knowing how one interacts with another still does not capture intelligence or memory. Think also of Adam Smith’s metaphor of the "invisible hand"—the ability of the free market to allocate resources and serve society despite the fact that each agent merely seeks his own interest.

          Reductionism as a way of understanding and modelling complex systems is therefore a scientific error: a method which worked in many physical instances but which was erroneously transposed to fields where it was not appropriate. Further, if reductionism is a mistaken approach merely due to its obfuscation of the holistic nature of complex systems, it is made even worse by the fact that even the individual, nonlinear relationships between the parts themselves are difficult to capture mathematically. For instance, there are no equations for clouds or for their exact relationships with the oceans, and thus climate models must use approximations.

          In economics, models must assume that economic agents act rationally or tend towards maximum efficiency. Yet, people do make irrational economic decisions. This can affect the validity of the models enormously. Orrell explains that models of complex systems are very sensitive to small errors in the approximate equations, notably because these systems are host to an extremely delicate balance between opposing forces and feedback mechanisms, where a slight imbalance in their representation has big effects on the accuracy of the models’ projections. Approximations are thus one of the main sources of forecast error—an error that grows with time as the system adopts a path that departs from the one projected.

          Put another way, computer programs must necessarily follow pre-determined, well-defined rules, whereas the behaviours of humans or of the natural world are often based on perpetually evolving rules, or on no rules at all. This means that the world within a model can only evolve towards the outcome set by the rules contained in the program, a mere description of what would happen if those rules were followed in the real world, holding all else constant.

          Finally, a scientific theory’s validity lies in its ability to survive testing, and models can only be tested against past experience. If such tests can work for simpler problems, they cannot work for complex systems where non-linearity and emergent properties such as adaptation mean that the past is no indication of the future. Even if the models are set to "predict" past data, they are still blind about what is to come. The test is therefore hopelessly flawed: predictions can always succeed in predicting the past, but this test is irrelevant if history does not repeat itself. And without a worthy test, any theory can seem to work.

          All in all, complexity theory gives us a more rigorous insight into what was before a more intuitive perception of the difficulty of accurately forecasting the economy, as it addresses the very mathematical aspects of the issue. Now, knowing this, what should be our course of action? To keep making predictions that we know cannot be saved from error, and nevertheless act on their basis, or stop using them and adopt a more stochastic approach where we would use the available information, aware of the limits to our knowledge? If "the study of the characteristics of complex dynamic systems is showing us exactly why limited knowledge is unavoidable [and] confronts us with the limits of human understanding,"(4) learning our limits is then an actual scientific discovery. To disregard this advancement would be foolish and unscientific. And yet, it seems that this is what economic forecasters are paid to do by our most powerful public institutions.

In the land of the blind, the one-eyed man is king


“Predictions of the future are never anything but projections of present automatic processes and procedures, that is, of occurrences that are likely to come to pass if men do not act and if nothing unexpected happens; every action, for better or worse, and every accident necessarily destroys the whole pattern in whose frame the prediction moves and where it finds its evidence.


–Hannah Arendt(5)


          Quantitative financier, former Wall Street trader and now bestselling author Nassim Taleb also took mainstream economics to task in his book, The Black Swan,(6) which was translated into dozens of languages and was named one of the 12 most influential books of the post-WW2 period by the Sunday Times.(7)

          Taleb made a fortune during the 2008 financial crisis betting against the models, as he understood that they discounted the "improbable" risk of systemic failure. He now uses his fame to good cause, warning us about the folly of guiding entire economies with wrongheaded economic models and theories. He is most emphatic about the dangers presented by economic forecasters, suggesting that "[a]nyone who causes harm by forecasting should be treated as either a fool or a liar. Some forecasters cause more damage to society than criminals."

          Taleb points to Paul Samuelson as the father of mainstream economics as it is currently taught in academia. Samuelson’s textbook, Economics: An Introductory Analysis, was first published in 1948 as one of the first American textbooks to explain the principles of Keynesian economics, and led to today’s intensified use of quantitative methods in economic analysis. As of today, his book still reigns in colleges and is now in its 19th edition. Taleb’s reflection on Samuelson’s legacy is unequivocal:

In orthodox economics, rationality became a straitjacket. Platonified economists ignored the fact that people might prefer to do something other than maximize their economic interests. This led to mathematical techniques such as "maximization," or "optimization," on which Paul Samuelson built much of his work. [...] I would not be the first to say that this optimization set back social science by reducing it from the intellectual and reflective discipline that it was becoming to an attempt at an "exact science." By "exact science," I mean a second-rate engineering problem for those who want to pretend that they are in the physics department—so-called physics envy. In other words, an intellectual fraud.

          Taleb argues against the Nobel Prize in economics for the damage it has done through its beatification of mistaken ideas about prediction and risk management; incorrect economic theories can be devastating and should never become gospel in such an uncertain environment. Forecasting methods create a false sense of security, or worse, send people in the wrong direction. Colleges then exacerbate the problem by teaching these Nobel-approved ideas as orthodoxy.(8)

"If we are wise, recent developments may help us to rediscover the role of the economist as a holistic observer rather than as a quantitative advisor. Perhaps the economist can then reclaim his credibility and assume a useful role which will not put him in a position where he is expected to blindly advise the king on what to do because he pretends that he can read the future."

          The "Ludic fallacy" is what Taleb calls the misuse of statistics that work in casino games to model real-life situations and their risk prospects mathematically. He refutes the validity of predictive models in complex situations where these statistical methods do not work, pointing out that the mathematical purity of such models fails to take into account certain key ideas such as the impossibility of possessing all relevant information and the fact that small unknown variations in the data can have a huge impact.

The rise of scientism


“The trouble with the world is that the stupid are cocksure and the intelligent are full of doubt.


–Bertrand Russell, logician and pacifist, co-author of the Russell-Einstein manifesto


“One hundred percent.


–Ben Bernanke, chairman of the Federal Reserve, on his confidence that the Fed will control inflation


          Doubt and epistemology have become anathema to modern (or scientific) central planning, if only because they disturb the illusion that things can be properly handled.(9) Yet one of the fathers of Western philosophy, Socrates, illuminated a timeless principle when he declared that what he knew was the fact of his own ignorance (a correction to the extravagance that we can know something truly or completely), and when he considered himself wiser than another man because he did not fancy himself as knowing what he did not know. For Socrates, doubt and curiosity were characteristics of the wise.

          It is curiosity that propelled the brightest minds of the past to question myths and dogmas. With the might of the scientific method, religion and superstition were deposed along with their explanations of many phenomena in fields such as astronomy, chemistry or physics. Come the 19th century, what still remained for human reason to solve and what still aroused curiosity was society itself and its behaviours. And intoxicated by the successes of Newtonian natural sciences so far, it was no surprise that a few clouded minds soon prescribed the same methods to explain "social physics."

          Hence came Auguste Comte (1798-1857), the father of sociology (originally coined "physique sociale") and an associate of Henri de Saint-Simon, the French early socialist. Recognising that the natural sciences had accomplished so much but that humanity remained clueless about social problems, Comte believed there was a next and ultimate phase of social evolution which was yet to come, namely Positivism (referring to the possibility of explaining all things, including the social, with the help of the scientific method). Comte realized that sociology was a more "complex" science, but believed that its problems could be solved using the same procedures that solved less complex phenomena. So in the Positivist age, through a social science based on quantitative and mathematical thinking, which he considered the centrepiece of all science, Comte saw a future world where the age of abstract rights (the Enlightenment era) would be replaced by a more modern period where society would be centrally planned by a scientific elite, empowered by its mastery of a new synthesizing science of man. Individual rights would be obsolete, replaced by duties in a world where experts "know best."

          Comte’s beliefs did have a profound influence on scientific and sociological thinking. If his Positivist religion gained little traction, his idea that society can be fixed through the scientific method beguiled many and is now widely held. Yet strangely, there never was any evidence to demonstrate that the traditional methods of the natural sciences were fit for examining complex social phenomena. It has just been assumed. This creates another strange situation in which those who believe most fervently in the power of science are proceeding under an assumption which was never scientifically demonstrated.

          In a way, Comte invented modern central planning. Marx and Hegel, among others, were likely influenced by him (or by his mentor St-Simon) and after their theories had spread, Positivists soon described themselves as Marxists.(10) On the belief that social problems could be fixed under an empirical "scientific" approach, grandiose plans of social reorganization were pushed forward in Europe and in the United States, enjoying support among social scientists and progressive leaders. Socialism became hugely popular and took over several countries. Woodrow Wilson supported eugenics, and many others praised Mussolini and/or the communists. These were instances of groundbreaking social engineering experiments after all. Those who opposed them were living in the past, fighting against progress and solutions to human ills.

          No matter how different in practical terms the kinds of central planning are now (Keynesianism, neo-classical economics, socialism, etc.), they still all spring from the same root: the idea that society can be mathematically reconstructed like a mechanical phenomenon and that the traditional methods of the natural sciences can be applied to fix its problems as if society were a laboratory-like controllable environment.

          Scientism is not only the ghost behind socialism, but hides behind all forms of contemporary central planning. It has already caused much harm, but the worst may still be ahead. Economics, as a social science connecting all and everything, is a hegemonic instrument. As the elder Rothschild said, "give me control of a nation’s money and I care not who makes her laws." Economics is Comte’s "physique sociale." Once it is widely believed that in its mastery or domestication by a select few lies the solution to the world’s every problem, liberty becomes a secondary issue, as the march towards utopia must prevail.

          This involves enormous implications for the fate of freedom (or abstract rights) and since this discussion is inevitably to be considered on scientific grounds, Hayek also sought to draw our attention back to epistemology, for through our ignorance of the limits of science, we expose ourselves to destroying much that we hold dear. Hayek’s final book, The Fatal Conceit, addresses the erroneous belief that man can shape society according to his wishes. As he observed, many progressives, socialists and central planners would have avoided their action plans if they could have really known the results in advance.

The Pretence of Knowledge

          If we are wise, recent developments may help us to rediscover the role of the economist as a holistic observer rather than as a quantitative advisor. Perhaps the economist can then reclaim his credibility and assume a useful role which will not put him in a position where he is expected to blindly advise the king on what to do because he pretends that he can read the future. But to paraphrase MIT’s Ricardo Caballero, macroeconomics as a field of study has been transformed over the decades from a verbal discussion of the real world to a discussion based on quantitative analysis of an alternate world.(11) This transformation appears difficult to reverse, as "modern" macroeconomics models are now at the core of economics faculties and their use has become a precondition for publication in academic journals since the 70s.(12)

          Yet, the 2008 financial crisis should strike us as evidence of the failure of mainstream macroeconomics. As late as February 2008, Ben Bernanke was still claiming that "my baseline outlook involves a period of sluggish growth, followed by a somewhat stronger pace of growth starting later this year as the effects of monetary and fiscal stimulus begin to be felt." So today, when quantitative easing programs prompted by mathematical models of the economy are championed by the same person who did not see the train coming from ten yards away, how confident can we be about his latest prediction that he will know how to control the dangers of inflation?

          Unfortunately, not very. Complex systems are characterized by decentralized, bottom-up governance. Order and direction of the whole are the result, but not the design, of the various relationships between the elements. Top-down management forced upon complex systems hampers their normal functioning, if only because the rule-makers cannot gain mastery of all the relevant, dispersed and elusive information which would be necessary to carry out decisions that would match the system’s holistic "intelligence." Therein lie the conflicts between legislation and common law, between central planning and spontaneous order, between authoritarianism and liberty. From this viewpoint, the re-emergent Austrian School of economics stands alone in its understanding of the benefits of decentralizing social and economic decisions.(13)

          My argument would not be complete without a return to what is perhaps the most eloquent exposition of the shortcomings of blindly applied quantitative methods in economics, namely Hayek’s Nobel Prize Lecture. Since I cannot better communicate Hayek’s points with my own words, my initial idea was to reproduce a few important excerpts. Obviously, though, there were too many, and I elected to reproduce only his introduction, which could just as well have been written today.

          My first hope is that readers who are unfamiliar with this lecture will be prompted to read it. My second and highest hope is that the lessons of prudence, humility and sound science which Hayek hoped to teach us will finally gain traction in an era when they are needed more than ever and when our understanding of complex phenomena is now more in sync with Hayek than it was when he delivered his timeless lecture in Sweden, decades ago:

Prize Lecture
Lecture to the memory of Alfred Nobel, December 11, 1974

The Pretence of Knowledge

The particular occasion of this lecture, combined with the chief practical problem which economists have to face today, have made the choice of its topic almost inevitable. On the one hand the still recent establishment of the Nobel Memorial Prize in Economic Science marks a significant step in the process by which, in the opinion of the general public, economics has been conceded some of the dignity and prestige of the physical sciences. On the other hand, the economists are at this moment called upon to say how to extricate the free world from the serious threat of accelerating inflation which, it must be admitted, has been brought about by policies which the majority of economists recommended and even urged governments to pursue. We have indeed at the moment little cause for pride: as a profession we have made a mess of things.

It seems to me that this failure of the economists to guide policy more successfully is closely connected with their propensity to imitate as closely as possible the procedures of the brilliantly successful physical sciences—an attempt which in our field may lead to outright error. It is an approach which has come to be described as the "scientistic" attitude—an attitude which, as I defined it some thirty years ago, "is decidedly unscientific in the true sense of the word, since it involves a mechanical and uncritical application of habits of thought to fields different from those in which they have been formed." I want today to begin by explaining how some of the gravest errors of recent economic policy are a direct consequence of this scientistic error.

1. Science, Philosophy and Religion: A Symposium, published by the Conference on Science, Philosophy and Religion in Their Relation to the Democratic Way of Life, Inc., New York (1941).
2. Strogatz, S. H., Sync: The Emerging Science of Spontaneous Order, Hyperion, 352 pages (2003).
3. Orrell, D., The Future of Everything: The Science of Prediction, Basic Books, 464 pages (2006).
4. Cilliers, P., "Why We Cannot Know Complex Things Completely," Emergence, 4(1/2), 77-84 (2002). His optimistic comments about models, though, appear misguided, if only because he seems to believe that the models could become as complex as the systems themselves. This seems an erroneous stance since increasing a model’s resolution usually adds to uncertainty, as every agent and feedback effect cannot be precisely represented through data or mathematical equations (such as clouds or human feelings)5. Harendt, H., On Violence, Houghton Mifflin Harcourt, 106 pages (1970). Pages 6-8 are of particular interest to the subject of predictions and scientific central planning.
6. Taleb, N. N., The Black Swan: The Impact of the Highly Improbable, Random House, 366 pages (2007).
7. Appleyard, B., "Books that Helped to Change the World," The Sunday Times (July 19, 2009).
8. Cox, A., "Blame Nobel for crisis, says author of 'Black Swan'," Reuters (September 28, 2010).
9. The inspired idea to juxtapose the two citations above is from Adam Sharp, from Wealth Daily.
10. See for instance Curtis, M., Marxism: The Inner Dialogues, volume 1 (2nd edition), pp. 27-31.
11. Caballero, R. J., Macroeconomics after the Crisis: Time to Deal with the Pretense-of-Knowledge Syndrome, MIT Department of Economics Working Paper No. 10-16 (September 27, 2010).
12. See this October 6, 2010 Forefront interview with former Federal Reserve governor Laurence Meyer.
13. Austrian School economists were notably early in apprehending that complex systems are incomputable when they fought the economic calculation debate in the early 20th century.