WHAT DO WE KNOW
FOR CERTAIN ABOUT UNCERTAINTY?
Keynote Address:
Peter Lewin, University of Texas at Dallas
THE LEGATUM INSTITUTE
Charles
Street Symposium
SUNDAY, 10 JUNE, 2012
Chesterfield
Hotel
Thank you very much to the Legatum
Institute and to Jeffrey Gedmin, Robert Hahn, Dalibor Rohac and Hemal Shah
and all the organizers of this inaugural Charles Street Symposium. I am honored
to have been invited and delighted to be part of this exciting venture.
My subject tonight is uncertainty and what we know
about it. Surprise, mystery, anxiety and, of course, profit, are all
implications of this phenomenon that we call uncertainty. Pretty much everything we know about humans in their
social lives is connected to uncertainty, the
fact that we do not know what is going to happen; people plan, they relish,
they fear, but they don’t really “know” for sure what is going to happen. As
George Shackle would say, we live in the fleeting present, imperfectly
remembering the past, anticipating the uncertain future. In that sense the
uncertain future is very much part of the present, and it shapes everything we
are and everything we do.
So uncertainty refers to a “lack of knowledge” to the
not-knowing. And we know that we don’t know. No less an authority than Donald
Rumsfeld informs us:
The truth is, there are
things we know, and we know we know them – the known knowns. There are things
we know that we don’t know – the known unknowns. And there are unknown
unknowns; the things we do not yet know that we do not know.
My topic tonight concerns this “meta-knowledge” – this
knowledge of not-knowing. What does it mean to know that we don’t know? What is
the nature of this knowledge, and what are its implications?
On this our teachers tell us different things? Permit
me a brief account of my own experience. I came of age as an economist under
the tutelage of Ludwig Lachmann. For him uncertainty was of the radical variety
and it was lethal for the standard neoclassical framework. Unless economics as
a discipline could figure out how to incorporate this real, this radical,
uncertainty, it was doomed to irrelevance at best, and to the propagation of arrogantly
administered disastrous economic policy at worst. Later, as I discovered the
extensive work of Friederich Hayek, this approach was reinforced – though, of
course, Lachmann and Hayek differ in some important respects. Hayek’s Nobel
prize lecture on the ‘Pretense of Knowledge’ is the culmination of a particular
way of thinking about uncertainty.
In September of 1972, I arrived at the University of
Chicago as a Ph.D. student. My first class in price theory was taught by Milton
Friedman. Frank Knight had very recently passed away, and my introduction to
Friedman featured a box of unsold copies of Risk,
Uncertainty, and Profit that had been brought from Knight’s office. These
free copies were made available to us students on a first-come-first -served
basis; and so it came to pass that the copy of Knight’s masterpiece that I have
on my bookshelf is the one I received that day. But, more important for our
subject tonight, is that, after the books were distributed, Friedman then presented
us, his new students, with a short discourse about Knight’s central idea. He
explained that Knight had thought that there was a fundamental difference
between risk and uncertainty, because the latter could not be cast in a probability
framework. Knight makes a distinction between decision-making contexts in which
the list of possible outcomes is known, and probabilities can be assigned to
the elements of this list; and contexts in which the list of possible outcomes
is unknown. It may be, of course, that though the list of outcomes is known,
their probabilities of occurring are not. This is a sort-of intermediate
position between risk and uncertainty – but is perhaps closer to risk than
uncertainty because at least the decision-maker can guess at the probabilities.
The case of unknown, unimagined outcomes, is categorically different. It is genuine uncertainty and it is
ubiquitous. It is the basis for entrepreneurial action and for profit.
In what seems to me a momentous turn, Friedman thought,
as he told us that day, that Knight was mistaken, because, since the advent of modern
statistical theory including Bayesian theory, we now know that it’s all a
matter of how you set it up. Friedman suggested, and, of course this is the
basis of the famous Friedman-Savage expected-utility choice theory, that all
decisions were informed by a kind of subjective-probability analysis that
allows the theorist to model decisions as
if they were sampling from hypothetical probability distributions. The
probabilities must add up to 100%, because whatever the decision-maker does not
know about the possible outcomes can be allocated to a residual category which
takes up the remaining, unallocated probability. It may be that this prior
probability-distribution is not very informative – it may be a very diffuse
prior distribution – but, in principle, says Friedman, there is no difference
between risk and uncertainty.
What is being reflected here, in the different
perspectives of Hayek and Friedman, is a difference in methodology that is wider
than the Grand Canyon. Friedman is implementing the latter-day Chicago maxim,
“if you can’t measure, measure anyway” because if you can’t measure you don’t
really know. Hayek would of course say that if you try to measure the
ummeasurable you engage in a pretense of knowledge. Less elegantly, the
proverbial drunk man looking for his keys under the lamppost, comes to mind.
Though, confused at the time, as might be expected of a
very average, unsophisticated graduate student, I have now come to think that
Friedman’s approach is an instance of the wider “abuse of reason” that occupied
Hayek for many decades. This refers to the presumption of economists, and some
other social scientists, that what the physical sciences had taught us was
that, everything is knowable, at least in principle, and that scientific
progress consists of discovering those constant relationships among underlying
variables, which correspond to real-world phenomena. In short, scientific
progress consists of simply finding out how things work and we can do this by
observing the world and its regularities.
Obviously, I don’t have the time, nor the inclination,
to examine this fundamental issue tonight. I will just say that the Friedman
(mainstream) position has come under attack from many directions, and is
something shared by most, if not all, of the various schools of heterodox
economics. We social scientists are starting to get comfortable with the idea
that there is a lot that is unknown and is likely to remain so for a while, as
well as the much more revolutionary idea, that there is a lot that is
unknowable. So before we begin in earnest we need to dispose of this
obligatory, well-known distinction between risk and uncertainty, which, I
believe to be very real. The well-defined outcome of a game of chance is
fundamentally, categorically different from the multitude of unknown and
unknowable outcomes that we face every day as part of the unfolding of time.
Novelty, surprise, the unimaginable are real. And it is this type of uncertainty
that I will be talking about.
So I suggest that the first thing we know about
uncertainty, is that it is real and it is radical. But what does that mean
exactly? Does it mean simply and only that we don’t know what can and will happen? Does it mean that what will happen
not yet determined? Or both? Is it just a matter of epistemology? Or is
uncertainty a matter of the world itself, of ontology? On this one, I am going
to punt. I don’t know. I have no knowledge of quantum mechanics and I have no
understanding of what it means to say that a particle has the potential to be a
wave but that this is not yet determined. In economics, in the work of Hayek,
Lachmann, Mises, Keynes, Schumpeter, Simon, Kirzner, Taleb and many others we
have the implication that there are certain things that we cannot know because
they are part of very complex processes. We may understand these processes, and
we may recognize outcomes as part of a wide range of permissible and
intelligible outcomes, but we cannot “know” these processes sufficiently to
predict outcomes in any detail, for example by predicting the values of certain
measurable variables. So, the question we are begging and ignoring is
basically; is this complexity a matter of knowledge or is the world intrinsically unpredictable,
uncomputable, undecidable. Such are the debates in the field of modern “complexity
studies,” and so I will leave it to them. What matters for us, it seems to me,
is that uncertainty is real and we know this for sure.
What else do we
know about uncertainty? Well we know that uncertainty is unavoidable.
But it is not irremediable. The consequences of uncertainty can be mitigated.
Most basically, there is insurance. For the life-insurance company, death is an
instance of a class of homogeneous instances whose occurrence can be assigned a
probability. The insurance company faces a situation akin to risk. For the
individual, by contrast, death is a single unique event – a matter of extreme
uncertainty. So the individual can leverage the difference to mitigate the
consequences, at least for his heirs. In other ways, we act to minimize the consequences
even of events that we cannot imagine, except to say that they are “bad”. And I
think here there is a distinction to be made between unknowable, unimaginable future
events and knowable possible
categories of consequences that can result. And this may give us substantial
theoretical traction, as in the use of agent-based modeling that the advent of
the computer age has made so attractive.
We know also
that uncertainty is uncomfortable, well mostly. Sometimes we like it,
like when we read a mystery novel, or watch a football game. We don’t want to
know what is going to happen. That would spoil the experience for us. Also, if uncertainty
implies that the outcome is likely to be good, or when it postpones the arrival
of something bad, we may welcome uncertainty. But in many other respects,
uncertainty is definitely unpleasant. It is responsible for the anxiety we
feel. And we know that in some cases this can be debilitating. In other, less
extreme, cases it can be very costly.
On a recent visit to Tbilisi, Georgia I saw an
interesting strategy for reducing uncertainty. The traffic lights are equipped
with timers – they count down the seconds till the next change. In this way the
motorist knows exactly when the light is going to change. Apparently this has
reduced road-rage and car accidents. Is this an unintended metaphor? All action
is planned action, by definition. It presupposes the categories of means and
ends and thus causality. Uncertainty tends to disrupt this connection, to make
our planning more difficult and fill us with apprehension. This is no more true
than in the context of economic policy. Providing “timers” – solid constraints
and interpretable signals - for economic policy could reduce the uncertainty we
feel about it.
The words I quoted from Donald Rumsfeld were spoken in
the context of assessing the consequences of going to war, and trying to make
decisions about what to do next at each turn of events. It is but a graphic
example of economic policy decision-making under real uncertainty. In this
context, perhaps the most significant and startling implication of uncertainty
is that it threatens the value-fact divide, the very possibility of wertfrei economics. Making
informed policy-decisions in a world in which the consequences are radically
uncertain, means that what you decide depends crucially on where you put the
burden-of-proof – what you consider to be the default position, what you
require to be disproved before action can be taken. The simplest, and most
relevant example is the identification of a so-called “market-failure” prior to
deciding that policy intervention is necessary. Will this imply proving that a
market-failure exists, or, in other words, disproving the assumption that the
market is efficient? Or will it imply, proving that the market is efficient and
disproving the assumption that it is not? Whichever you choose, because we are
dealing with real uncertainty and complex processes, it is probably impossible
to disprove the null-hypothesis. Your choice, therefore, will depend not on
disinterested science, but, rather on which type of error you consider most
egregious and wish to avoid, in other words on your values. The greater the degree of causal ambiguity, the greater the importance of the burden-of-proof.
And uncertainty is all about causal ambiguity.
Uncertainty can mean not only not-knowing what is going
to happen. It can also imply not knowing how to deal with what happens when it
does, not knowing how to act or how to fix something or how make something you
will need. So, much of our discussion about uncertainty is about mitigating and
coping mechanisms. An interesting case is the case of novelty in economic life.
Economic growth and development are very much about the discovery and
introduction of new products and services, new production methods, new
resources and materials, new modes of organization, etc. Gaining and
maintaining a competitive advantage in the marketplace involves being
innovative. How can you plan for this? Much research these days is about what
kinds of organizations are most likely to be innovative. It’s a big subject. I
will say only that it is very much about the management of knowledge-generation
within organizations in the same way that Hayek perceived of knowledge-generation
in a decentralized market economy.
In fact, uncertainty is, from another systemic perspective,
necessary, desirable and empowering. Without it life would be dull and it would
be static. There would be no entrepreneur and there would be no profit, there
would be no novelty, no need to figure out how to cooperate, so no Sesame
Street moments, no mystery and, of course, no conferences like this. It would
be a world completely different from our own. One of the thematic outcomes of
my own work is the conviction that the world is becoming more uncertain all the
time, even while our ability to deal with this uncertainty is improving
dramatically. We generate and experience a greater degree of complexity and
uncertainty precisely because we can handle it. In many ways that is the story
of the information age.
But how, exactly, does this happen? I think the key is
being able to predict, with sufficient degree of accuracy, what other people
will do under various circumstances. As Adam Smith pointed out, each of us is
dependent upon the cooperation of thousands of other people for even the most
simple accoutrements of life. That is the miracle of the market. But the market
would not work without a shared common language, without a firm shared basis in
the law, in custom, in the norms we follow every day without even thinking
about it. These social realities are what we often refer to as social
institutions and they are what allow us to act in a world of radical
uncertainty. So, I will end with an brief explanation of how I believe this
occurs.
It’s all a matter of inconsistent expectations which lead to uncertain and complex
situations. As Hayek pointed out, the expectations of economic agents are
‘data’ for action. Expectations relating to uncertain future events imply the
introduction of the unpredictable expectations of others upon who actions the
success of our actions depends. Inconsistent expectations can mean inconsistent
actions, disorder and disequilibrium. The new-classical counterrevolution was
built on pointing out that expectations are best understood to be ‘rational’ –
hence not all that unpredictable. And that debate is still not quite over. But
I want to go in a different direction.
I suggest we need to unpack the concept of
‘expectations’ and ask the question ‘expectations of what?’ Obviously individuals
have expectations about many different things. But, only some of these are
likely to differ much across individuals. Those that form the basis of
institutions, expectations about the ‘rules of game’ are likely to be very
uniform across individuals. We may say that these expectations are informed by
knowledge of the ‘social laws’ concerning how others will (almost) invariably
behave in given situations. These expectations are likely to be very congruent.
By contrast those expectations relating to the outcomes of introducing a new
product, a new advertising approach, a new technology, a new competitive
strategy, are not informed by such ‘hard’ knowledge. These are likely to be all
over the place. Yet, such actions will not
be deterred on account of the diffuseness of expectations and the uncertainty,
the causal ambiguity that this implies. The entrepreneur acts precisely because
he believes he is different and he knows better than the rest, absent which
there would be no profit in it. Thus, somewhat paradoxically, predictability in one sphere of action is
the necessary ingredient for coping with its absence (novelty) in another
sphere. (Loasby 1991, 1994). We may invoke, as is often done, the analogy
of a sports game, the fact that the outcome (the score, and the details of the
action) cannot be predicted with any degree of certainty does not prevent the
game from being played. On the contrary, it is this very unpredictability that
adds to its attraction. What is
predictable are the consequences of any infringement of the rules of the game,
the fact that the losers will probably accept the result peacefully and so on.
And it is this that allows the game to be played.
But whence the “rules of the game”? Another analogy - an
individual walks across the mall full of snow and leaves a trail of footprints.
Someone following him finds it helpful to walk in his footsteps (pun intended).
Those who follow do the same and eventually they make a path through the snow
that is of benefit to all who walk it (Kirzner 1992: Introduction). The
original trailblazer is an unintentional
institutional entrepreneur. The general principle here is the operation of network-effects – the more people use
the network the greater the benefits for each (Liebowitz & Margolis 1994).
Social institutions are complex phenomena and they are networks. A network of
this kind is one in which the individuals who participate benefit from a shared
(frequently tacit) understanding of how to proceed – a common standard (like a
telephone technology, a language group, a religious group, a commonly accepted
means of payment, a system of commercial laws, etc.). These ‘external benefits’
are the network effects that imply that there is feedback from individual
action to other individuals, in the direction of producing uniform expectations
regarding each other’s behavior (choices).
In this way, we can provide plausible choice-theoretic
arguments showing how individuals perceive the benefits of choosing common
modes of behavior. In other words, social institutions are likely to emerge
spontaneously from individual action and to grow spontaneously to an optimum
size. And there are many examples of convergent social processes, perhaps the
most familiar being the emergence of money (Menger 1871).
Uncertainty exists, we know that for certain, but we
also know that it has many aspects. Experience tells us that while we cannot
predict who will succeed and who will not, while we cannot predict which
products will emerge and be popular, while we cannot foresee the nature of
future technologies, living in liberal democracies we strongly believe that the
process will be peaceful and will be orderly. The fruits of this dynamic process depend crucially on our
(predictable) willingness to accept the consequences of its unpredictability.
That willingness is the vital predictable part. Indeed, as with other such complex
adaptive orders, what we have in the market process is the emergence of an
unpredictable but intelligible ‘order’ and we are able to explain this process
in a readily accessible and intuitive way deriving from our understanding of human
action. Uncertainty is real, it is unavoidable, but not irremediable, it is
uncomfortable but it is necessary and it does not preclude us from acting. All
of this we know for certain.
References:
Kirzner, I.
(1992). The Meaning of Market Process. London and New York: Routledge
Loasby, B.
(1991) Equilibrium and Evolution: An Exploration of Connecting Principles in
Economics Manchester: Manchester University Press.
______ (1994)
‘Evolution Within Equilibrium’, Advances in Austrian Economics. 1
