Mainstream economics — the triumph of ideology over science

from Lars Syll

Research shows not only that individuals sometimes act differently than standard economic theories predict, but that they do so regularly, systematically, and in ways that can be understood and interpreted through alternative hypotheses, competing with those utilised by orthodox economists.

Senate Banking Subcommittee On Financial Institutions Hearing With StiglitzTo most market participants — and, indeed, ordinary observers — this does not seem like big news … In fact, this irrationality is no news to the economics profession either. John Maynard Keynes long ago described the stock market as based not on rational individuals struggling to uncover market fundamentals, but as a beauty contest in which the winner is the one who guesses best what the judges will say …

Adam Smith’s invisible hand — the idea that free markets lead to efficiency as if guided by unseen forces — is invisible, at least in part, because it is not there …

For more than 20 years, economists were enthralled by so-called “rational expectations” models which assumed that all participants have the same (if not perfect) information and act perfectly rationally, that markets are perfectly efficient, that unemployment never exists (except when caused by greedy unions or government minimum wages), and where there is never any credit rationing.

That such models prevailed, especially in America’s graduate schools, despite evidence to the contrary, bears testimony to a triumph of ideology over science. Unfortunately, students of these graduate programmes now act as policymakers in many countries, and are trying to implement programmes based on the ideas that have come to be called market fundamentalism … Good science recognises its limitations, but the prophets of rational expectations have usually shown no such modesty.

Joseph Stiglitz

The rational expectations hypothesis — one of the cornerstones of mainstream economics — presupposes, basically for reasons of consistency, that agents have complete knowledge of all relevant probability distribution functions. And when trying to incorporate learning in these models — trying to take the heat of some of the criticism launched against it up to date — it is always a very restricted kind of learning that is considered. Learning where truly unanticipated, surprising, new things never take place, but only rather mechanical updatings — increasing the precision of already existing information sets — of existing probability functions.

Nothing really new happens in these ergodic models, where the statistical representation of learning and information is nothing more than a caricature of what takes place in the real-world target system. This follows from taking for granted that people’s decisions can be portrayed as based on an existing probability distribution, which by definition implies the knowledge of every possible event (otherwise it is in a strict mathematical-statistical sense not really a probability distribution) that can be thought of taking place.

But in the real world, it is — as shown again and again by behavioral and experimental economics — common to mistake a conditional distribution for a probability distribution. Mistakes that are impossible to make in the kinds of economic analysis — built on the rational expectations hypothesis — that mainstream economists are such adamant propagators for. On average, rational expectations agents are always correct. But truly new information will not only reduce the estimation error but actually change the entire estimation and hence possibly the decisions made. To be truly new, information has to be unexpected. If not, it would simply be inferred from the already existing information set.

In rational expectations models, new information is typically presented as only reducing the estimated parameter variance. But if new information means truly new information it actually could increase our uncertainty and variance (information set (A, B) => (A, B, C)).

Truly new information gives birth to new probabilities, revised plans, and decisions – something the rational expectations hypothesis cannot account for with its finite sampling representation of incomplete information.

In the world of rational expectations, learning is like being better and better at reciting the complete works of Shakespeare by heart — or at hitting the bull’s eye when playing dart. It presupposes that we have a complete list of the possible states of the world and that by definition mistakes are non-systematic (which, strictly seen, follows from the assumption of ‘subjective’ probability distributions being equal to the ‘objective’ probability distribution). This is a rather uninteresting and trivial kind of learning. It is a closed world learning, synonymous with improving one’s adaptation to a world that is fundamentally unchanging. But in real, open-world situations, learning is more often about adapting and trying to cope with genuinely new phenomena.

The rational expectations hypothesis presumes consistent behavior, where expectations do not display any persistent errors. In the world of rational expectations, we are always, on average, hitting the bull’s eye. In the more realistic, open systems view, there is always the possibility (danger) of making mistakes that may turn out to be systematic. It is because of this, presumably, that we put so much emphasis on learning in our modern knowledge societies.

The unwillingness — sometimes obviously for ideological reasons — to take genuine uncertainty seriously has made much of ‘modern’ economics more or less irrelevant. The alleged ‘rigor’ and ‘precision’ of the analyses have not taken us one single iota closer to understanding what goes on in our modern economies.

Those who want to build macroeconomics on microfoundations usually maintain that the only robust policies and institutions are those based on rational expectations and representative actors. As yours truly has tried to show in On the use and misuse of theories and models in economics there is really no support for this conviction at all. On the contrary. If we want to have anything of interest to say on real economies, financial crises and the decisions and choices real people make, it is high time to place macroeconomic models building on representative actors and rational expectations microfoundations in the dustbin of pseudo-science.

But if this microfounded macroeconomics has nothing to say about the real world and the economic problems out there, why should we care about it? The final court of appeal for macroeconomic models is the real world, and as long as no convincing justification is put forward for how the inferential bridging de facto is made, macroeconomic modelbuilding is little more than hand-waving that gives us a rather little warrant for making inductive inferences from models to real-world target systems. If substantive questions about the real world are being posed, it is the formalistic-mathematical representations utilized to analyze them that have to match reality, not the other way around.

The real macroeconomic challenge is to accept genuine uncertainty and still try to explain why economic transactions take place — instead of simply conjuring the problem away by assuming rational expectations and treating uncertainty as if it was possible to reduce it to stochastic risk. That is scientific cheating. And it has been going on for too long now.