Economics Student Prize Night
  12 December, 2011

  "The pretence of knowledge" (after Hayek)

  Simon D. Angus
  Dept. of Economics
  Monash University

Good evening, visiting friends and supporters of the Department, colleagues, and importantly -- the deserving student prize-winners we are here to celebrate tonight.

I was given a broad brief. (Read a blank sheet.) So, .. I have interpreted the invitation to speak tonight as having the following aim: in a room full of professional, academic and aspiring Economists, to provide some gristle for our conversations.

I intend to do that.

Let's begin.

I have entitled the talk, 'The pretence of knowledge' (after Hayek).


"The particular occasion of this [talk], combined with the chief practical problem which economists have to face today, have made the choice of its topic almost inevitable. … Economists are at this moment called upon to say how to extricate the free world from the serious threat of accelerating [sovereign debt and unemployment, which,] it must be admitted, [have] 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." [1]

… So begins (only slightly updated) the Lecture to the memory of Alfred Nobel, delivered by Frederick von Hayek, December 11, 1974.

Of course, the 'chief practical problem' of the day was accelerating inflation, and would eventually see 'the free world' having to deal with a two-pronged assault: hyper inflation, and high unemployment.

But the present sovereign debt crisis and attendant unemployment in Europe and the US seems no less serious to the stability of the international (not just 'free world') economy.

.. We could add to 'the present crisis' by pointing to the growing unease in the polity of the developed world, symbolised most recently by the 'Occupy Wall-street' (and its international variants) movement. .. "We are the 99% they shouted". It's easy to fob them off as rebels without a cause latching onto the latest ticket to camp in the public squares of civilised cities the world over ...

But at heart, they were voicing ferocious dissatisfaction with an ever-freer market system -- encouraged by us, the Economists. That system, which undoubtedly has seen the wealth tide rise for many, has also seen the average US family income growth rate more than halve in a generation (2.5% to ~1%), whilst growth in the richest 1% of incomes has tripled (~1.1% to 3.8%).[2]

The first response to such a charge is to deny. Economists are good at that.

I know that there will be many here tonight who will not wish to have blame attributed to their beloved field for the present, protracted, and powerful drags on the economy in Europe and the US (thankfully not here), nor the idiosyncratic problems of the US welfare system (or lack of it) -- I suppose you will want to say that 'we, in the profession':
 - never encouraged the wilds of the financial system during the '90s, and naughties;
 - that we never encouraged the closer and tighter inter-linkages of the economies and financial systems of the world;
 - that we never added our voice to any round of 'fiscal stimulus' or 'quantitative easing' .. not even in the depths of the global financial/economic crisis;
 - that we never advocated cutting government taxes in the name of freeing up the market to do its thing;
 - or more importantly, that we saw the Global Financial Crisis coming and warned whomever would listen months before it came upon us.

I suspect that our normal response, in 'the profession' is rather more political than we would like to admit -- we will gladly take the credit when it seems like our policy suggestions have brought good outcomes, but when the graphs turn south .. and well-south, we fall back on our friendly 'political economy' plus 'public-choice' formulaic excuse -- "the governments of the day don't implement our good ideas .. it's the politicians bending to special-interest, stupid." (Not the economists.)

But I am going to assume that within your Economic heart, you have a kernel of uneasiness -- an uneasiness that stems from the clean theory of the market and human behaviour that we inevitably hold fast to, versus the seemingly unknowable, indescribable complexity (with its messy, unpredictable transitions in aggregate behaviour to boot), that we see out the window.

Hayek identified the problem in '74 as that of economists being 'scientistic'. That is, in trying to appeal to the mainstream of science, and produce the kind of analytical 'laws' based on the empirical 'facts' of the sciences, Economists had over-fitted their models to that which they could measure.

Ergo, the standard connection between aggregate demand and unemployment was fashioned not from causal relationships that Hayek felt were validated by "everyday experience", rather, they were the product of high-level, approximate meta-linkages arising measurable empirical data. Hayek wrote thus,

"The correlation between aggregate demand and total employment, for instance, may only be approximate, but as it is the only one on which we have quantitative data, it is accepted as the only causal connection that counts. On this standard there may thus well exist better "scientific" evidence for a false theory, which will be accepted because it is more "scientific", than for a valid explanation, which is rejected because there is no sufficient quantitative evidence for it."

My claim is that we have not learnt much since Hayek spoke these words in '74. That the profession must feel a significant portion of the blame for the present crisis. And that we must embrace new methods to treat the fundamental problem.

But my conjecture -- and one which I want the students to hear loudly -- is that the coming century will provide the Economic Sciences with the kind of data which will vastly improve our ability to empirically validate the kind of 'obvious linkages' that were dismissed by the profession in Hayek's day, and sadly, I feel, still are.

So what was it that Hayek would have preferred we see beneath the brutish empirical abstractions and approximations of the classical theory?

In a word: complexity.

Hayek again,

"… The social sciences, like much of biology but unlike most fields of the physical sciences, have to deal with structures of essential complexity, i.e. with structures whose characteristic properties can be exhibited only by models made up of relatively large numbers of variables. Competition, for instance, is a process which will produce certain results only if it proceeds among a fairly large number of acting persons."

Hayek goes on to draw a distinction between 'unorganised complexity' and 'organised complexity'. Unorganised complexity, he argued, were systems of many interacting parts, but whose pattern of interactions were a weak force for organisation. One could abstract from such interactions and treat the system as a collection of statistical distributions. Gasses and well-mixed fluids would be examples.

On the other hand, 'organised complexity', describes those systems where the pattern of interactions matter. Statistical descriptions will not do.

Statistical description of these systems will, at best, produce what Hayek called "mere pattern predictions -- predictions of some of the general attributes of the structures that will form themselves, but not containing specific statements about the individual elements of which the structures are made up."

He goes to explain why describing relative prices as deviations from 'equilibrium' is a doomed goal. The information regarding relative prices and wages is distributed amongst (I quote), "every one of the participants in the market process -- a sum of facts which in their totality cannot be known to the scientific observer, or to any other single brain." (Which is the triumph of the market system.)

"But because we, the observing scientists, can thus never know all the determinants of such an order, and in consequence also cannot know at which particular structure of prices and wages demand would everywhere equal supply, we also cannot measure the deviations from that order; nor can we statistically test our theory that it is the deviations from that "equilibrium" system of prices and wages which make it impossible to sell some of the products and services at the prices at which they are offered."

To sum up, Hayek famously called the Economic Sciences' unwillingness to engage with this kind of complexity and instead rest on "scientistic" theory, "the pretence of knowledge"; a state of affairs Hayek could not bring himself to join.

So much for Hayek's assessment. Does it still apply?

Of course, there are movements at the margins -- experimental economics, behavioural economics, micro-founded macro (I won't mention 'neuro-economics') -- but in the main, our textbooks at all levels still promote a theory of behaviour at the micro- and macro- level unchanged from that which was taught a generation ago.

The 'essential complexity' paradigm is all but missing from our descriptions of the Economy.

One need only ponder for a moment the hard distinction we still draw between 'micro-' and 'macro-' economics (in the class-room at least, if not in research) to see that we just haven't grasped the fundamental thrust of Hayek's critique. .. For if the economy really does display 'organised complexity' as Hayek argued then to speak of analysis which operates at the micro-, or macro-, levels of abstractions only, would be as non-sensical as a biologist trying to describe the behaviour of the bee hive by studying single bees (on the one hand), or probability distributions of hive consumption and production (on the other).

What we miss here is the organisation of the economy (or bee-hive for that matter).

I am proud to say that on this score, our Department at Monash University has an enviable track-record, yet one which is perhaps not as well known as it should be. The late Xiaokai Yang (himself nominated twice for the Nobel Prize), developed an elegant and powerful alternate theory of the Economy while gracing the very same Ming Wing of Monash Clayton that many of us call home.  Yang's theory did, indeed, emphasise and analyse the organisational structure of the economy first, and only then studied the individual profit-maximising decisions of the agents with each structure thereafter. By itself, Yang's theory is not the answer to Hayek's critique, but at least it trains attention on the organisational aspects of economic production, and so provides a ready  starting point for a richer theory of the kind Hayek had in mind.

But apart from a theory of organisation, there is another front on which Economic complexity can be assailed. Hayek's powerfully creative mind saw it even then, in 1974. Summing up the problem he writes,

"A theory of essentially complex phenomena must refer to a large number of particular facts; and to derive a prediction from it, or to test it, we have to ascertain all these particular facts. Once we succeeded in this there should be no particular difficulty about deriving testable predictions - with the help of modern computers it should be easy enough to insert these data into the appropriate blanks of the theoretical formulae and to derive a prediction. The real difficulty, to the solution of which science has little to contribute, and which is sometimes indeed insoluble, consists in the ascertainment of the particular facts."

For Hayek, building the theory -- as Yang has shown -- is not the essential problem that we face, it will ever be in gaining access to the vast array of "particular facts". Not just amassing them, but getting them into a form which can be used to test the developing theory.

Hayek saw this challenge as over-whelming for his time, but in his mind's eye he foresaw a future generation who may indeed have access, at once, to databases of "particular facts", and at the same time, to the computing power required for their analysis and exploration, thus enabling the 'easy' derivation of predictions.

I do not pretend to suggest that we are there yet, but I want to argue that we are very nearly there.

Let me briefly walk you through three powerful examples. [[slides]]

* First -- Google Trends [3]

* (Aside: Google n-Gram viewer --> exploring culture with 15,000,000 books) [4]

* Second -- stress testing the global trade network. [Foti et al., May 2011] [5]

* Third -- identifying complexity in trading patterns, and opportunities for development. [Hidalgo and Hausmann] [6]
 - PNAS: 2009
 - The Atlas of Economic Complexity: July, 2011

In each of these examples we find gigabytes (perhaps terabytes) of data -- here are the "particular facts" that Hayek knew would be vast in their scope, but would provide the kind of richness that could begin to test theory and explore the vexed notion of prediction.

In each of these examples, the skills on display have themselves been inter-connected. None of these papers are authored by pure-economists. In each case, physicists, computer-scientists, biologists, visualisation specialists, software-developers and statisticians have teamed up with Economists to pool their skills and drive powerfully at the data. The obvious implication is that this new data game will be played in teams.

I believe that these examples (and several others like them) demonstrate the need for tomorrow's Economists to swallow their considerable pride, and learn to work with others from outside of their field.

They will need to read more widely -- understanding the mental models that a physicist, biologist and computer scientist carries with them, so that when the time for team play comes, the Economists will have enough essential conceptual language to communicate with their newfound colleagues.

In terms of our teaching, now is the time for Economic Departments to train their charges in computational and numerical approaches. MIT has already recognised this fact and is actively encouraging courses in 'Algorithmic thinking' be part of core first-year (yes! .. first-year) training. We should do the same.

Indeed, with Yang's legacy, the collective 'mind' of Monash faculty is better prepared than perhaps any other research intensive university in Australia to take the lead in embracing such a curriculum.

The future of Economics will continue to be battled on multiple methodological fronts -- none of this says that theoretical or experimental or statistical expertise will not be part of the future. But if we are unable to handle the data -- and masses of it -- or worse, unwilling to work in multi-disciplinary teams on the new data-sets that Google, Amazon, the NBER, or the ATO will inevitably make available, then put simply, as a profession, we will be left behind.

(As an aside, to the professional Economists in the room, who wonder what ever did happen to your beloved Hal Varian -- of "Intermediate Microeconomics" textbook fame -- he's Google's chief Economist running analysis on Google Trends which I showed you earlier. .. Evidently, some in the profession have been early adopters of the new paradigm.)

I don't need to remind us how some in the physical sciences already view Economics. We seem close to being like the 'Irish' (or worse, Kiwis) when it comes to joke-making. .. But our isolation from the physical science must end. To continue with a mental model of non-overlapping kingdoms and inculcate this within our teaching structures, is to hitch our wagon to the past.

Not only will we not benefit from the interactions that a more connected, complexity embracing, and data-rich collaboration afford, but worse, our colleagues in the other sciences will do 'bad economics' as they mis-handle and abuse the newfound Economic data and its bevy of 'particular facts'.

In this embarrassingly debt-laden time, let's not add more reasons for Hayek to be grinding his teeth from the grave.

To close -- let me issue some challenges.

To those of us in the teaching faculty, our challenge is to see a prize night in a decade which awards various undergraduate and post-graduate units that serve the larger aim of the complexity century we are entering. Perhaps 'Data-mining 101', or 'Essential complexity 2', will be awarded alongside, 'The Economics of Networks', 'Non-linear Dynamics 3', and 'Evolutionary Economics 2'. That is, to work towards a curriculum that trains our students for the data-, visualisation-, computational-, and complexity- challenges that they will face as the Economists of the new paradigm.

To those of you who employ our graduates I will be interested to hear your perspective. I have an inkling that many of you are well aware of the huge datasets that you are presently sitting on, accumulating, or salivating over. I also suspect that you wish graduates had the very skills that I have been describing coupled with a first-rate Economists way of thinking about the world. To take the path that I am describing, we would obviously need your support and encouragement.

Lastly, and importantly, to the talented Economic students of this evening -- you are perhaps best placed to take advantage of the times. The new skills that will be needed to work effectively with large data-sets within the complexity paradigm are best acquired when young. Embracing complexity, avoiding the pretence of knowledge, and learning to work across fields is the challenge I give to you this evening.

We all look forward to hearing of your future successes.


[1] von Hayek, Frederick, "The Pretence of Knowledge", Lecture to the memory of Alfred Nobel, December 11, 1974. URL:

[2] See "Why the 99% are unhappy", Alan Kohler's graphs (orig. New York Times), URL: .

[3] Google Trends, see: .

[4] Google n-Gram Viewer, see source:; and associated paper, Michel, J. B., Shen, Y. K., Aiden, A. P., Veres, A., Gray, M. K., The Google Books Team, Pickett, J. P., et al. (2011). Quantitative Analysis of Culture Using Millions of Digitized Books. Science, 331(6014), 176–182.

[5] See: Foti, Pauls & Rockmore, "Stability of the world trade web over time - an extinction analysis", (May, 2011), p.14. (available at: arXiv:1104.4380v2).

[6] See: Hidalgo, C., & Hausmann, R. (2009). The building blocks of economic complexity. Proceedings of the National Academy of Sciences, 106(26), 10570.