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The pretence of knowledge.
An ever-lasting lesson
Roberto Tamborini
University of Trento, Department of Economics,
Via Inama 5, 38100 Trento, Italy,
[email protected]
In the May 2008 issue of AER (Papers and Proceedings of the
American Economic Association), David Colander, Peter Howitt, Alan
Kirman, Axel Leijonhufvud, and Perry Mehrling expound the view that
macroeconomics should be recast in a conception of the market economy as
a complex adaptive system (CAS). The reason being that
what makes macroeconomics a separate field of study is the complex properties of
aggregate behavior that emerge from the interaction among agents. Since in a
complex system aggregate behavior cannot be deduced from an analysis of
individuals alone, representative agent models fail to address the most basic
questions of macroeconomics (p. 236)
The paper by Colander et al. is representative, and provides the
relevant references, of "a strong undercurrent of opposition to modern
macroeconomic models" − as they say − that is gaining momentum in the
widespread methodological debate prompted by the current world crisis.
Quite correctly, Colander et al. understand that this challenge should
eventually be faced on the grounds of economic policy. Can the science of
economic systems provide better services to society (in the form of public
policy) if it is projected into the orbit of the "Complexity Vision" (Colander
ed., 2000)?
An illustrious predecessor
The 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.
This is not, as it might appear, the complaint of a heterodox
economist about the evils of "the mainstream" in the aftermath of the 2008
crisis. It is the opening sentence of the Nobel Prize Memorial Lecture that
Friedrich A. von Hayek gave in 1974 (reprinted in the American Economic
Review, 1989, 79, pp. 3-7). It was entitled "The Pretence of Knowledge".
2
At that time, economics and the economic profession were living
through another period of crisis generated by a sense of confusion and
impotence in the face of "stagflation", that combination of recession,
unemployment and high inflation which plagued the world economy in the
early '70s. The mainstream was then the US brand of Keynesian macrotheory with the new tools of large macro-econometric models used in
support of policy-making according to what would become to be known as
"optimal stochastic control" (OSC). This approach was growing on the
presumption, and conveying the idea, that the system's behaviour can be
fully known and controlled up to "user-friendly" ("white noise") random
shocks. Here the analogy is with a control system that assists a missile
launch unit, the pilot of the Shuttle, or the driver of a Formula 1 car (which,
alas, sometimes go out of control anyway).
Hayek had been a fierce opponent of Keynes's theory ever since its
appearance. However, most of his speech was not aimed against this theory
per se, but rather against the advent of that particular approach to macropolicy, and the way in which it was inducing economists erroneously to
apply economic theories of any sort to the real world. This is what renders
Hayek's lecture a milestone worth recalling today.
Hayek was one of the path-breaking thinkers in the theory of CAS,
first in his neuro-physiology studies and then in economics. His
epistemology was based on the irreducible difference between the physical
world and the social world, the former being amenable to reduction to, and
quantification of, relatively few laws based on systematic or frequentist
observations, the latter not being amenable to such treatment owing to its
"self-organized complexity"1.
Organized complexity here means that the character of the structures showing it
depends not only on the properties of the individual elements of which they are
composed, and the relative frequency with which they occur, but on the manner in
which the individual elements are connected with each other. In the explanation of
the working of such structures [… we] require full information about each element
1 It should noted that, though there is a variety of nuances of it, the complexity
view is now common to different fields of modern natural sciences, from physics to
biology to chemistry. Hayek himself had contributed to this paradigmatic shift in
the view of the physical world too (see e.g. 1952). Hence his contrast between the
methods of the natural sciences and of the social sciences is limited to the natural
sciences that do not deal with complex phenomena, or, more probably, refers to the
naïve view of the natural sciences that so much attraction has always exerted on
economists.
3
if from our theory we are to derive specific predictions about individual events (p. 4,
emphasis added).
The core of Hayek's thought about the free-market organization of
society then comes into play: no one in society can ever have access to the
entirety of the dispersed and localized information regarding each single
individual element. ‘No one’ means economists as well.
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 […] The real difficulty, to the solution of which science
has little to contribute […] consists in the ascertainment of the particular facts (pp.
6, 7).
The "difficulty" here is epistemological in nature; that is to say, it
cannot be overcome by merely developing new statistical techniques. For it
is an illusion "that we can use technique for the determination and
prediction of the numerical values of those magnitudes [… and] the vain
search for quantitative or numerical constants" (p. 5).
Note that in these passages Hayek was referring to the intrinsic
limits imposed on economic theory in general – including general
equilibrium theory and not Keynesian theory alone – by the very nature of
its object. He was wont to quote Pareto's dictum that we cannot predict
from general equilibrium theory the price of bread tomorrow. Hayek did not
imply that no theory of general and abstract patterns and properties of
these structures is possible. He indeed regarded competitive general
equilibrium theory as a legitimate and consistent proof that a self-organized
order is possible in such a complex structure as a free-market economic
system. What Hayek emphatically rejected was the claim that this theory
can be brought directly to real world data and aimed at detailed prediction
and control in the "scientistic fashion" criticized above.
Unlike the position that exists in the physical sciences, in economics and other
disciplines that deal with essentially complex phenomena, the aspects of the events
to be accounted for about which we can get quantitative data are necessarily
limited and may not include the important ones […] In the study of such complex
phenomena as the market, which depends on the actions of many individuals, all
the circumstances which will determine the outcome of a process […] will hardly
ever be fully known or measurable [...]
Without such specific information about the individual elements we shall be
confined to […] pattern predictions: predictions of some of the general attributes of
the structures, but not containing specific statements about the individual
elements of which the structures will be made up" (pp. 3, 4; italics added)
4
"Modern economics"
In the hands of the then upcoming new generation of Chicago freemarket theorists, those who laid the bases for today's mainstream, Hayek's
epistemology was first misinterpreted and then entirely betrayed2. They
argued that the origin of the failure and crisis of economics was simply that
Keynesian theory offered poor guidance to macro-policy because "it lacked
rigorous microfoundations". They then replaced Keynesian theory with
general equilibrium theory, while also giving tremendous impetus to the
OSC approach to macro-policy. The search for stable and predictable
numerical relationships among aggregate variables that had misled the
Keynesian macro-econometric models was replaced by the search for stable
and predictable "deep parameters" (preferences, etc.) ruling individual
optimizing behaviour − the prescriptive side of the so-called Lucas Critique.
The "representative agent" short-cut may be seen as an attempt to
circumvent Hayek's objection that all the relevant information is simply not
attainable. This methodological apparatus has eventually been endorsed
also by the "New Keyensian" macroeconomic school that was born to
contrast the "no-policy" implications of the new Chicago school and reinstate
the role of stabilization policies. The result is a common methodological
framework (Blanchard (2008)), the so-called dynamic stochastic general
equilibrium (DSGE) models that can be regulated by means of OSC
instruments, promising to transform policy-making from art into science
(e.g. Clarida et al. (1999)). This methodology is orthogonal to the CAS view.
It is telling that this complete misconception of Hayek's epistemology
occurred most blatantly at its very core: the (un)predictability and
(un)controllability of the system at the level of individual behaviours. In fact,
the usual argument in favour of rooting macro-theory in the neoclassical
micro-theory of fully rational and optimizing individuals is that this makes
the macro-level fully predictable (up to external random shocks). Yet, as a
few important papers have shown (e.g. Kirman (1992)), the representative
agent is a deceptive short-cut leading nowhere. Underground (in both senses
of the word) theoretical and empirical research, and to some extent reality,
have vindicated Hayek's view and have, in parallel, shaken the foundations
2 An early warning came from Frydman and Phelps’s (1983) book on the rational
expectations hypothesis.
5
of the reductionist programme. Indeed, the opposite idea has emerged,
namely that the deeper we penetrate into the micro-structure, the more we
find the shifting sands of heterogeneity, bounded rationality, and all sorts of
behavioural
vagaries.
Delving
into
individual
behaviour
and
microfoundations has turned out to be very much like a one-way journey
with no return ticket towards the meso- or macro-surface. Thus, the
"rigorous" microfoundations claimed by the reductionists now appear not to
be "serious" scientifically, whereas the "serious" microfoundations
discovered by scientific investigation of human behaviour are hardly
susceptible to "rigorous" aggregative procedures.
Defenders of the DSGE-OSC apparatus entertain an intermittent
attitude towards the issue of prediction and control. On the one hand, most
practictioners subscribe to the popular version of Friedman's
instrumentalism (whatever model is good insofar as it gives good predictions
of the data, net of normally distributed errors, of course). Nowadays, as a
response to the allegation of not being able to predict the crisis, it is often
heard the argument that these models never promised accurate predictions
of booms and busts. In fact, these are the "exogenous shocks" part of the
story, and as such they are removed out of the reach of scientific economics.
Yet this delimitation of the pretence of scientific economics is far away from
Hayek's, who instead draws it at the very core of what the standard
macroeconomist calls the "structural part" of the model.
Another oft-heard argument is that seismologists cannot predict the
occurrence of earthquakes with precision, but this does not make seismology
a useless science. The same applies to meteorologists and long-term weather
forecasts. But the unpredictable residuals buffeting DSGE economies
attended by OSC tools are totally different from the unpredictable
phenomena ("emergent properties") arising from system-wide interactions
among heterogeneous particles that characterize CAS. For instance,
seismologists, meteorologists, etc., do have a "mainstream" theory of
earthquakes and hurricanes (which by the way are complex system theories)
and they do know why we cannot predict and control these phenomena
accurately (because we cannot know and control all the conditions of the
relevant complex dynamic processes). Mainstream economists do not have
such a theory of economic disasters: these are simply non-existent in their
6
theory.3 There must be something special that mainstream economists fear
when thinking about economic disasters but which mainstream
seismologists or meteorologists instead find so attractive in natural
disasters.
Where can we go from here?
If − simply not to trespass the limits of this short note − we ignore the
controversial co-existence in Hayek's thought of his complexity view with his
confidence in the possibility that "pattern predictions" of economic CAS may
include the standard general-equilibrium theorems, many modern followers
of the complexity view are probably ready to subscribe to the substance of
the statements quoted above. On this background, in their plea for
economics as a science of CAS, Colander et al. (2008) recognize that
At present, [these models] are still far too simple to bring directly to policy; they
are, at best, suggestive […] How should one undertake macro policy today? Our
answer is that policy economists need to go back to the engineering approach that
economists used up until the 1940s and 1950s. That engineering approach does not
search for scientific understanding: it searches for models that shed light on the
problems at hand […] One can, and should, search for relationships among
macroeconomic variables without worrying about the behavioral foundations of
those relations (p. 239).
Though sympathetic with the complexity view, I think that the
approach to economic policy proposed above is not fully consistent with the
epistemology of CAS, and it raises more problems than it offers solutions.
First, it is questionable that it is difficult to bring CAS models to
policy only because they are still "too simple". Spectacular technical
progress in computer-based simulations of artificial CAS has strongly
improved the scientific status of this methodology, and the ability of
researches to understand how controlled changes in the structure or the
inputs of the system produce new configurations and outcomes (Epstein,
1999). However, the hallmark of the complexity view is that complexity is a
characterization of how a (possibly "simple") system works by way of
"adaptive, self-organizing, out-of-equilibrium processes" (Foley, 2003, p. 2),
not a characterization of the way in which the system is designed.
Consequently, as was argued by Hayek, the difficulty with policy in the real
3 Lucas (2004) recognized this point with clarity.
7
world − of which we are not the designer nor do we know the exact encoded
programme − is not merely technical: it is more radical, and epistemological
in nature. Increasing the "complication" of models of complexity will not
necessarily improve policy for CAS.
Second, any complexity scientist would certainly be upset at hearing
that he or she is not searching for scientific understanding. The immediate
questions that come to mind are: scientific understanding of what, and of
what kind? 4 One may say that the rules, practices, and self-understanding
of scientific method pursued by complexity scientists may be different in
nature from other methods that are pursued in the study of non-complex
entities − and certainly in the no-complexity vision of economic systems. In
particular, in a genuine CAS perspective, the search for scientific
understanding of human behaviour should certainly not be abandoned by
economists. Indeed, it should be fostered on an entirely renewed, nonautistic, relationship with the true sciences of human behaviour5.
Third, a more data-driven, empirically-oriented relationship going
from organized statistical evidence to theory, and not only the other way
round, is certainly welcome (e.g. Hoover et al., 2008)6. Yet, if economic
systems are CAS, policy analysis, or policy action, based on black-box
econometrics may turn out be no less a dangerous illusion than that based
on would-be microfounded econometrics. To mention just one problem, a
typical implication of the complexity view is that it is generally unsafe to
project past empirical regularities into the future7.
Finally, but most importantly, "policy engineering", if it is not just an
infelicitous wording, indicates a concept that cannot be the right solution
since it is precisely the same operative arm of optimally controllable
systems (Mankiw, 2006) that was originally criticized by Hayek8. He
instead established the so-called "Ignorance Principle" as one of the key
4 Thorough reflections on these questions can be found in Epstein (1999).
5 As a matter of fact, this necessity is recognized by Colander et al. (2008)
elsewhere in the paper and in many other writings in the CAS literature. See for
instance Colander (ed., 2000), Epstein (1999).
6 Delli Gatti et al. (2008) exemplify how a CAS can produce a whole set of empirical
regularities observed in the actual system, and vice versa, how the same empirical
regularities can be used as a means to discipline the design of the artificial model.
7 Also, it is doubtful whether data generated by CAS are subject to classical
probability laws as it is assumed by any econometric theory .
8 For a detailed and formalized analysis of this issue see Velupillai (2007)
8
ingredients of economic policy in the complexity view (Brock and Colander
(2000, pp. 82 ff.)). The policy makers are part of the system, and no part of
the system can track with certainty (or in terms of well-defined probability
distributions) the consequences of its own actions on the whole.
If man is not to do more harm than good in his efforts to improve the social order,
he will have to learn that in this, as in all other fields were essential complexity of
an organized kind prevails, he cannot acquire the full knowledge which would
make mastery of the events possible (Hayek, 1974, p. 7).
These considerations imply neither that there cannot be scientific
method in the policy analysis of CAS nor that the only consistent policy
implication of the complexity view is the faith in self-regulating markets9.
Keynesian "policy engineering", after a couple of decades of apparent
successes, suddenly failed to master stagflation, but Hayek had succumbed
to Keynes in the debate over the causes and remedies of the Great
Depression not for merely theoretical reasons. And he probably would
succumb today, given the almost ubiquitous quest and support for
Keynesian measures to curb the crisis10. Hayek was probably over-confident
in the ability of market systems to always find their way out of instability or
disasters without a sufficiently robust visible hand, or he was overpessimistic about the ability of the visible hand to never find a way to keep
the system from going astray. After all, chemistry, biology or physics of
complex phenomena are not merely contemplative activities. These
phenomena do present some empirical regularities at the observable macrolevel, and, for most of them, scientists have been able to achieve sufficiently
robust knowledge to the benefit of human beings. However, I think that
economists of all persuasions should come to terms with the Ignorance
Principle challenge to "policy engineering" in CAS.
Will economists be ready to retreat from the unprecedented pretence
of knowledge inbuilt in, and conveyed by, policy analysis in the optimal-
9 On these points see e.g. Brock and Colander (2000), Velupillai (2007).
10 It is telling that Lucas (2009), in his defense of mainstream macroeconomics,
has argued that its good health is proven by its ability to indicate the right policies
to policy makers, so that "the recession is now under control and no responsible
forecasters see anything remotely like the 1929-33 contraction in America on the
horizon". Notably, Lucas here makes explicit reference to distinguished economists
in the policy-making arena, such as Ben Bernanke, who are academically
associated with the so-called "New Keynesian" brand of DSGE models, which is not
his most preferred brand.
9
control-DSGE fashion? Will they be ready to accompany their theories,
predictions and prescriptions with a clear statement of their limits and
potential damage if mistaken, as one can find in medicine dosage
instructions? Is this deontology consistent with the self-constructed image of
the modern economist as social engineer (Mankiw, 2006)? And, on the other
hand, will institutional and political authorities be willing to pay pecuniary
and non-pecuniary rewards to technicians falling short of the technical
certainties of an engineer?
References
Blanchard, O. J. (2008), "The State of Macro", NBER Working Paper, n.
14259.
Brock W. A., Colander D. (2000), "Complexity and Policy", in Colander D.
(ed.), The Complexity Vision and the Teaching of Economics,
Aldershot, Elgar.
Clarida R., Galì J., Gertler M. (1999), "The Science of Monetary Policy: A
New Keynesian Perspective", Journal of Economic Literature, 37,
pp.1661-1707.
Colander D. (ed., 2000), The Complexity Vision and the Teaching of
Economics, Aldershot, Elgar.
Colander D., Howitt P., Kirman A., Leijonhufvud A. Meherling P. (2008),
"Beyond
DSGE
Models:
Toward
an
Emprically
Based
Macroeconomics", Papers and Proceedings of the American Economic
Association, American Economic Review, 98, 236-240.
Delli Gatti D., Gaffeo E., Gallegati M., Giulioni F., Palestrini M. (2008),
Emergent Macroeconomics, Wien, Springer.
Epstein J. H. (1999), "Agent-based Computational Models and Generative
Social Sciences", Complexity, 4, pp. 41-60.
Foley D. (2003), Unholy Trinity. Labor, Capital, and Land in the New
Economy, London, Routledge.
Frydman R., Phelps E. (1983, eds.) Individual Forecasting and Aggregate
Outcomes. Rational Expectations Examined, Cambridge, Cambridge
University Press.
Hoover K.D., Johansen S., Juselius K. (2008), "Allowing the Data to Speak
Freely: The Macroeconometrics of the Cointegrated Vector
Autoregression", Papers and Proceedings of the American Economic
Association, American Economic Review, 98, pp. 251-255.
Kirman A. (1992), "Whom or What Does the Representative Individual
Represent?", Journal of Economic Perspectives, vol.6, pp.117-136.
Lucas R.E. (2004), "Keynote Address to the 2003 HOPE Conference: My
Keynesian Education", in de Vroey M., Hoover K. (eds.), The IS-LM
Model: Its Rise, Fall and Strange Persistence", Durham, Duke
University Press.
10
Lucas R.E. (2009), "In Defence of the Dismal Science", The Economist,
August 8, www.economist.com
Mankiw N. G. (2006), "The Macroeconomist as Scientist and Engineer",
Journal of Economic Perpsectives, 20, pp. 29-46.
Velupillai K. (2007), "The Impossibility of an Effective Theory of Policy in a
Complex Economy". In: Complexity Hints for Policy . Colander D.,
Salzano M. (a cura di), Berlin, New York, N.Y.: Springer
von Hayek F. A. (1942), "Scientism and The Study of Society", Economica,
vol.IX, Aug.
von Hayek F.A. (1952), The Sensory Order, London, Routledge and Keegan
Paul.
von Hayek F.A. (1974), "The Pretence of Knowledge", Nobel Memorial Prize
Lecture, reprinted in American Ecconomic Review, 1989, 79, pp. 3-7.