LARS P. SYLL

On testing and learning in a non-repetitive world

The incorporation of new information makes sense only if the future is to be similar to the past. Any kind of empirical test, whatever form it adopts, will not make sense, however, if the world is uncertain because in such a world induction does not work. Past experience is not a useful guide to guess the future in these conditions (it only serves when the future, somehow, is already implicit in the present) … I believe the only way to use past experience is to assume that the world is repetitive. In a non-repetitive world in which relevant novelties unexpectedly arise testing is irrelevant …

These considerations are applicable to decisions in conditions of radical uncertainty. If the actions that I undertake in t0 will have very different consequences according to the eventual state of the world in t1, it is crucial to gather reliable knowledge about these states. But how could I evaluate in t0 my beliefs about the state of the world in t1? If the world were repetitive (governed by immutable laws) and these laws were known, I could assume that what I find out about the present state is relevant to determine how the future state (the one that will prevail) will be. It would make then sense to apply a strategy for gathering empirical evidence (a sequence of actions to collect new data). But if the world is not repetitive, what makes me think that the new information may be at all useful regarding future events? …

Conceiving economic processes like sequences of events in which uncertainty reigns, where consequently there are “no laws”, nor “invariants” or “mechanisms” to discover, the kind of learning that experiments or last experience provide is of no use for the future, because it eliminates innovation and creativity and does not take into account the arboreal character and the open-ended nature of the economic process … However, as said before, we can gather precise information, restricted in space and time (data). But, what is the purpose of obtaining this sort of information if uncertainty about future events prevails? … The problem is that taking uncertainty seriously puts in question the relevance the data obtained by means of testing or experimentation has for future situations.

Marqués’ book is a serious challenge to much of mainstream economic thinking and its methodological and philosophical underpinnings. A must-read for anyone interested in the foundations of economic theory.

To yours truly, Marqués’ book is especially important since it shows how far-reaching the effects of taking Keynes’ concept of genuine uncertainty really are.

Almost a hundred years after John Maynard Keynes wrote his seminal A Treatise on Probability (1921), it is still very difficult to find economics textbooks that seriously try to incorporate his far-reaching and incisive analysis of uncertainty, inductive inference and evidential weight.

The standard view in economics and statistics — and the axiomatic probability theory underlying it — is to a large extent based on the rather simplistic idea that ‘more is better.’ But as Keynes argues – ‘more of the same’ is not what is important when making inductive inferences. It’s rather a question of ‘more but different.’

Variation, not replication, is at the core of induction. Finding that p(x|y) = p(x|y & w) doesn’t make w ‘irrelevant.’ Knowing that the probability is unchanged when w is present gives p(x|y & w) another evidential weight (‘weight of argument’). Running 10 replicative experiments do not make you as ‘sure’ of your inductions as when running 10 000 varied experiments – even if the probability values happen to be the same.

According to Keynes we live in a world permeated by unmeasurable uncertainty – not quantifiable stochastic risk – which often forces us to make decisions based on anything but ‘rational expectations.’ Keynes rather thinks that we base our expectations on the confidence or ‘weight’ we put on different events and alternatives. To Keynes, expectations are a question of weighing probabilities by ‘degrees of belief,’ beliefs that often have preciously little to do with the kind of stochastic probabilistic calculations made by the rational agents as modelled by ‘modern’ social sciences. And often we “simply do not know.” As Keynes writes in Treatise:

If different wholes were subject to different laws qua wholes and not simply on account of and in proportion to the differences of their parts, knowledge of a part could not lead, it would seem, even to presumptive or probable knowledge as to its association with other parts … In my judgment, the practical usefulness of those modes of inference … on which the boasted knowledge of modern science depends, can only exist … if the universe of phenomena does in fact present those peculiar characteristics of atomism and limited variety which appears more and more clearly as the ultimate result to which material science is tending.

Science according to Keynes should help us penetrate to “the true process of causation lying behind current events” and disclose “the causal forces behind the apparent facts.” Models can never be more than a starting point in that endeavour. He further argued that it was inadmissible to project history on the future. Consequently, we cannot presuppose that what has worked before, will continue to do so in the future. That statistical models can get hold of correlations between different ‘variables’ is not enough. If they cannot get at the causal structure that generated the data, they are not really ‘identified.’

How strange that writers of economics textbooks do not even touch upon these aspects of scientific methodology that seems to be so fundamental and important for anyone trying to understand how we learn and orient ourselves in an uncertain world. An educated guess on why this is a fact would be that Keynes concepts are not possible to squeeze into a single calculable numerical ‘probability.’ In the quest for quantities one puts a blind eye to qualities and looks the other way — but Keynes ideas keep creeping out from under the carpet.

Robert Lucas once wrote — in Studies in Business-Cycle Theory — that “in cases of uncertainty, economic reasoning will be of no value.”  Now, if that was true, it would put us in a tough dilemma. If we have to consider — as Lucas — uncertainty incompatible with economics being a science, and we actually know for sure that there are several and deeply important situations in real-world contexts where we — both epistemologically and ontologically — face genuine uncertainty, well, then we actually would have to choose between reality and science.

That can’t be right. We all know we do not know very much about the future. We all know the future harbours lots of unknown unknowns. Those are ontological facts we just have to accept. But — I still think it possible we can go for both reality and science, and develop a realist, relevant, non-ergodic economic science.

The biggest trouble with modern​ macroeconomics

The trouble is not so much that macroeconomists say things that are inconsistent with the facts. The real trouble is that other economists do not care that the macroeconomists do not care about the facts. An indifferent tolerance of obvious error is even more corrosive to science than committed advocacy of error.

Paul Romer 

New-Classical-Real-Business-Cycles-DSGE-New-Keynesian microfounded macromodels try to describe and analyze complex and heterogeneous real economies with a single rational-expectations-robot-imitation-representative-agent. That is, with something that has absolutely nothing to do with reality.

Opting for cloned representative agents that are all identical is of course not a real solution for analyzing macroeconomic issues. Representative agent models are — as I have argued at length here — rather an evasion whereby issues of distribution, coordination, heterogeneity — everything that really defines macroeconomics — are swept under the rug.

Of course, most macroeconomists know that to use a representative agent is a flagrantly illegitimate method of ignoring real aggregation issues. They keep on with their business, nevertheless, just because it significantly simplifies what they are doing.

Continuing to model a world full of agents behaving as economists — ‘often wrong, but never uncertain’ — is a gross misallocation of intellectual resources and time.

Keynes — en sällsynt fågel

Alfred Marshall skrev en gång att “bra ekonomer är sällsynta fåglar.” Det stämmer verkligen. En av dessa sällsynta fåglar var definitivt John Maynard Keynes.

Och nu har den norske ekonomiprofessorn Björn-Ivar Davidsen skrivit en bok om denne sällsynt kompetente ekonom, filosof, statstjänsteman, spekulant, utopist, universitetsmecenat, konstsamlare, med mera, med mera.

I boken får vi följa med på en resa i Keynes’ biografiska fotspår och får på så vis ta del av denne mångsidige intellektuelle gigants liv och leverne. Även om boken främst är biografiskt hållen, finns det, inte minst i de avslutande kapitlen, intressanta och välargumenterade resonemang kring vad som är kärnan i Keynes ekonomiska tänkande.

Nyttig läsning — inte minst för de som kanske läst lite nationalekonomi på något av våra universitet och därför tyvärr fått en fullständigt förvrängd bild av vad som var det omvälvande och revolutionerande i Keynes’ ekonomiska tänkande.

Med den här boken visar författaren att han själv också är en sällsynt fågel. Skrivkunniga akademiska ekonomer är en utrotningshotad art. Vilken tur att vi då kan läsa denna välskrivna och intressanta bok, skriven av en ekonom som uppenbart klarar av att skriva skön prosa.

Tag och läs!

Models and economics

Economics is a science of thinking in terms of models joined to the art of choosing models which are relevant to the contemporary world. It is compelled to be this, because, unlike the typical natural science, the material to which it is applied is, in too many respects, not homogeneous through time. The object of a model is to segregate the semi-permanent or relatively constant factors from those which are transitory or fluctuating so as to develop a logical way of thinking about the latter, and of understanding the time sequences to which they give rise in particular cases … Good economists are scarce because the gift for using “vigilant observation” to choose good models, although it does not require a highly specialised intellectual technique, appears to be a very rare one.

John Maynard Keynes (letter to Harrod, 1938)

Keeping the dream alive

For me, the study of asymmetric information was a very first step toward the realization of a dream. That dream was the development of a behavioral macroeconomics in the original spirit of Keynes’ General Theory. Macroeconomics would then no longer suffer from the ad hockery of the neoclassical synthesis, which had over-ridden the emphasis in The General Theory on the role of psychological and sociological factors, such as cognitive bias, reciprocity, fairness, herding, and social status. My dream was to strengthen macroeconomic theory by incorporating assumptions honed to the observation of such behavior …

Keynes’ General Theory was the greatest contribution to behavioral economics before the present era. Almost everywhere Keynes blamed market failures on psychological propensities (as in consumption) and irrationalities (as in stock market speculation). Immediately after its publication, the economics profession tamed Keynesian economics. They domesticated it as they translated it into the “smooth” mathematics of classical economics. But economies, like lions, are wild and dangerous. Modern behavioral economics has rediscovered the wild side of macroeconomic behavior. Behavioral economists are becoming lion tamers. The task is as intellectually exciting as it is difficult.

George Akerlof

Keynes’ core in​sight

But these more recent writers like their predecessors were still dealing with a system in which the amount of the factors employed was given and the other relevant facts were known more or less for certain … At any given time facts and expectations were assumed to be given in a definite and calculable form … The calculus of probability, tho mention of it was kept in the background, was supposed to be capable of reducing uncertainty to the same calculable status as that of certainty itself …

The fact that our knowledge of the future is fluctuating, vague and uncertain, renders Wealth a peculiarly unsuitable subject for the methods of the classical economic theory …

By “uncertain” knowledge, let me explain, I do not mean merely to distinguish what is known for certain from what is only probable … The sense in which I am using the term is that in which the prospect of a European war is uncertain, or the price of copper and the rate of interest twenty years hence … About these matters there is no scientific basis on which to form any calculable probability whatever. We simply do not know.

John Maynard Keynes

To understand real world ‘non-routine’ decisions and unforeseeable changes in behaviour, ergodic probability distributions are of no avail. In a world full of genuine uncertainty – where real historical time rules the roost – the probabilities that ruled the past cannot simply be assumed to be those that will rule the future.

Time is what prevents everything from happening at once. To assume that economic processes are ergodic and concentrate on ‘ensemble averages’ is not a sensible way for dealing with the kind of genuine uncertainty that permeates real-world economies.

What is important with the fact that real social and economic processes are nonergodic is the fact that uncertainty – not risk – rules the roost. Thinking about uncertainty in terms of ‘rational expectations’ and ‘ensemble averages’ has had seriously bad repercussions on the financial system.

Keynes’ uncertainty concept has an ontological founding. Of course this also has repercussions on the issue of ergodicity in a strict methodological and mathematical-statistical sense.

The most interesting and far-reaching difference between an epistemological and an ontological view on uncertainty is that if one subscribes to the former, you open up for the mistaken belief that with better information and greater computer-power we somehow should always be able to calculate probabilities and describe the world as an ergodic universe. As Keynes convincingly argued, that is ontologically just not possible.

To Keynes, the source of uncertainty is in the nature of the real – nonergodic – world. It has to do not primarily with the epistemological fact of us not knowing the things that today are unknown, but rather with the much deeper and far-reaching ontological fact that there often is no firm basis on which we can form quantifiable probabilities and expectations at all.

We have to accept that if we really want to be able to understand and analyze real-world phenomena we have to accept them on their own premisses. Our quest for knowledge should never decide how to perceive reality.

The most important and far-reaching premiss on which modern mainstream economics builds is the assumption that genuine uncertainty is reducible to calculable risk. Since this is not the case, modern mainstream economics is also totally useless.

Take the rational expectations assumption. Rational expectations in the mainstream economists’ world imply that relevant distributions have to be time independent. This amounts to assuming that an economy is like a closed system with known stochastic probability distributions for all different events. In reality, it is straining one’s beliefs to try to represent economies as outcomes of stochastic processes. An existing economy is a single realization tout court, and hardly conceivable as one realization out of an ensemble of economy-worlds since an economy can hardly be conceived as being completely replicated over time. It is — to say the least — very difficult to see any similarity between these modelling assumptions and the expectations of real persons. In the world of the rational expectations hypothesis, we are never disappointed in any other way than as when we lose at the roulette wheels. But real life is not an urn or a roulette wheel. And that’s also the reason why allowing for cases where agents make ‘predictable errors’ in DSGE models doesn’t take us any closer to a relevant and realist depiction of actual economic decisions and behaviours. If we really want to have anything of interest to say on real economies, financial crisis and the decisions and choices real people make we have to replace the rational expectations hypothesis and calculable risk with more relevant and realistic assumptions concerning uncertainty than childish roulette and urn analogies. Or as Quetelet once declared — “l’urne que nous interrogeons, c’est la nature.”

Economics — a science with wacky views of human behaviour​

There is something about the way economists construct their models nowadays that obviously doesn’t sit right.

The one-sided, almost religious, insistence on axiomatic-deductivist modelling as the only scientific activity worthy of pursuing in economics still has not given way to methodological pluralism based on ontological considerations (rather than formalistic tractability). In their search for model-based rigour and certainty, ‘modern’ economics has turned out to be a totally hopeless project in terms of real-world relevance.

If macroeconomic models — no matter of what ilk — build on microfoundational assumptions of representative actors, rational expectations, market clearing and equilibrium, and we know that real people and markets cannot be expected to obey these assumptions, the warrants for supposing that model-based conclusions or hypotheses of causally relevant mechanisms or regularities can be bridged to real-world target systems, are obviously non-justifiable. Incompatibility between actual behaviour and the behaviour in macroeconomic models building on representative actors and rational expectations microfoundations shows the futility of trying to represent real-world target systems with models flagrantly at odds with reality. As Robert Gordon once had it:

Rigor competes with relevance in macroeconomic and monetary theory, and in some lines of development macro and monetary theorists, like many of their colleagues in micro theory, seem to consider relevance to be more or less irrelevant.

Science and the quest for truth

In my view, scientific theories are not to be considered ‘true’ or ‘false.’ In constructing such a theory, we are not trying to get at the truth, or even to approximate to it: rather, we are trying to organize our thoughts and observations in a useful manner.

Robert Aumann

 

What a handy view of science.

How reassuring for all of you who have always thought that believing in the tooth fairy make you understand what happens to kids’ teeth. Now a ‘Nobel prize’ winning economist tells you that if there are such things as tooth fairies or not doesn’t really matter. Scientific theories are not about what is true or false, but whether ‘they enable us to organize and understand our observations’ …

Mirabile dictu!

What Aumann and other defenders of scientific storytelling ‘forgets’ is that potential explanatory power achieved in thought experimental models is not enough for attaining real explanations. Model explanations are at best conjectures, and whether they do or do not explain things in the real world is something we have to test. To just believe that you understand or explain things better with thought experiments is not enough. Without a warranted export certificate to the real world, model explanations are pretty worthless. Proving things in models is not enough. Truth is an important concept in real science.

Marx and Keynes on the contradictions of capitalism

Each capitalist, Marx noted, has an ambiguous relation to the workers. On the one hand, she wants the workers she employs to have low wages, since that makes for high profits. On the other hand, she wants all other workers to have high wages, since that makes for high demand for her products. Although it is possible for any one capitalist to have both desires satisfied, it is logically impossible for this to be the case for all capitalists simultaneously. This is a ‘contradiction of capitalism’ that Keynes spelled out as follows. In a situation of falling profit, each capitalist responds by laying off workers, thus saving on the wage bill. Yet since the demand of workers directly or indirectly is what sustains the firm, the effect of all capitalists’ simultaneously laying off workers will be a further reduction in profit, causing more lay-offs or bankruptcies.