Why Krugman and Stiglitz are no real alternatives to mainstream economics

from Lars Syll

Little in the discipline has changed in the wake of the crisis. Mirowski thinks that this is at least in part a result of the impotence of the loyal opposition — those economists such as Joseph Stiglitz or Paul Krugman who attempt to oppose the more viciously neoliberal articulations of economic theory from within the camp of neoclassical economics. Though Krugman and Stiglitz have attacked concepts like the efficient markets hypothesis … Mirowski argues that their attempt to do so while retaining the basic theoretical architecture of neoclassicism has rendered them doubly ineffective.

First, their adoption of the battery of assumptions that accompany most neoclassical theorizing — about representative agents, treating information like any other commodity, and so on — make it nearly impossible to conclusively rebut arguments like the efficient markets hypothesis. Instead, they end up tinkering with it, introducing a nuance here or a qualification there … Stiglitz’s and Krugman’s arguments, while receiving circulation through the popular press, utterly fail to transform the discipline.

Paul Heideman

Despite all their radical rhetoric, Krugman and Stiglitz are — where it really counts — nothing but die-hard mainstream economists, just like Milton Friedman, Robert Lucas or Greg Mankiw.

The only economic analysis that Krugman and Stiglitz  — like other mainstream economists — accept is the one that takes place within the analytic-formalistic modelling strategy that makes up the core of mainstream economics.

Irrational Exuberance — Robert Shiller’s modern classic

Lars Syll

At the beginning of the year 2000, a book titled Irrational Exuberance was published. The American economics professor and Nobel laureate Robert Shiller warned that the extensive deregulation in the financial market that had taken place since the Thatcher-Reagan era had led to a rapid credit expansion. Banks and financial institutions saw a skyrocketing increase in lending, and the pursuit of gaining larger market shares led to neglecting creditworthiness checks and accepting poor customer relationships. Above all, the values of IT stocks were disproportionately high. It would inevitably lead to a financial crisis.

Shiller was proven right. Less than two months after the book was published, the usual happened. The bubble burst, and the financial market crisis became a reality.

Because human memory is short, new concerns reappeared after a few years. The crisis that the American economy once again found itself in had its origins in the speculative bubble that developed in the American housing market between 1997 and 2006. Despite falling interest rates and construction costs, housing prices rose by an average of 85 per cent over the ten years when the bubble was inflated.

The underlying pattern is the same in almost all financial crises. For some reason, a shift occurs (war, innovations, new rules, and more) in the economic cycle that leads to changes in banks’ and companies’ profit opportunities. Demand and prices rise, pulling more and more parts of the economy into a kind of euphoria. More and more people get involved, and soon speculative frenzy – whether it’s about tulip bulbs, properties, or mortgages – becomes a reality. Sooner or later, someone sells to cash in their profits, triggering a rush for liquidity. It’s time to jump off the carousel and convert securities and other assets into cash. A financial emergency arises and spreads. Prices begin to decline, bankruptcies increase, and the crisis accelerates, turning into panic.

To prevent the final crash, credit is tightened, and calls for a lender of last resort who can guarantee the supply of the demanded cash and restore confidence arise. If that fails, the crash becomes a reality.

Economic methodology — Lawson, Mäki, and Syll

from Lars Syll

We are all realists and we all — Mäki, Cartwright, and I — self-consciously present ourselves as such. The most obvious research-guiding commonality, perhaps, is that we do all look at the ontological presuppositions of economics or economists.

Where we part company, I believe, is that I want to go much further. I guess I would see their work as primarily analytical and my own as more critically constructive or dialectical. My goal is less the clarification of what economists are doing and presupposing as seeking to change the orientation of modern economics … Specifically, I have been much more prepared than the other two to criticise the ontological presuppositions of economists—at least publically. I think Mäki is probably the most guarded. I think too he is the least critical, at least of the state of modern economics …

One feature of Mäki’s work that I am not overly convinced by, but which he seems to value, is his method of theoretical isolation (Mäki 1992). If he is advocating it as a method for social scientific research, I doubt it will be found to have much relevance—for reasons I discuss in Economics and reality (Lawson 1997). But if he is just saying that the most charitable way of interpreting mainstream economists is that they are acting on this method, then fine. Sometimes, though, he seems to imply more …

I cannot get enthused by Mäki’s concern to see what can be justified in contemporary formalistic modelling endeavours. The insights, where they exist, seem so obvious, circumscribed, and tagged on anyway …

As I view things, anyway, a real difference between Mäki and me is that he is far less, or less openly, critical of the state and practices of modern economics … Mäki seems more inclined to accept mainstream economic contributions as largely successful, or anyway uncritically. I certainly do not think we can accept mainstream contributions as successful, and so I proceed somewhat differently …

So if there is a difference here it is that Mäki more often starts out from mainstream academic economic analyses accepted rather uncritically, whilst I prefer to start from those everyday practices widely regarded as successful.

Tony Lawson

Tony Lawson and Uskali Mäki are both highly influential contemporary students of economic methodology and philosophy. Yours truly has learned a lot from both of them.

Robert Lucas (1937-2023)

from Lars Syll

Economic theory, like anthropology, ‘works’ by studying societies which are in some relevant sense simpler or more primitive than our own, in the hope either that relations that are important but hidden in our society will be laid bare in simpler ones, or that concrete evidence can be discovered for possibilities which are open to us which are without precedent in our own history. Unlike anthropologists, however, economists simply invent the primitive societies we study, a practice which frees us from limiting ourselves to societies which can be physically visited as sparing us the discomforts of long stays among savages. This method of society-invention is the source of the utopian character of economics; and of the mix of distrust and envy with which we are viewed by our fellow social scientists. The point of studying wholly fictional, rather than actual societies, is that it is relatively inexpensive to subject them to external forces of various types and observe the way they react. If, subjected to forces similar to those acting on actual societies, the artificial society reacts in a similar way, we gain confidence that there are useable connections between the invented society and the one we really care about.

Robert Lucas

Although plenty of more or less hagiographic obituaries have acknowledged Lucas ‘brilliance’ and ‘influence’, I find it more appropriate to just honestly address the reasons his whole project to reform macroeconomics was such a blatant failure.

Neither yours truly, nor anthropologists, will recognise anything in Lucas’ description of economic theory even remotely reminiscent of practices actually used in real sciences, this quote still gives a very good picture of Lucas’ methodology.

Weekend read – Minsky and Keynes show the way out of the crisis

from Lars Syll

American economist Hyman Minsky described capitalism as a “two price” system. On one side are asset prices—both financial, like government or corporate bonds, and physical like residential or commercial property. On the other, there are consumer prices—goods and services that determine current output and consumer price inflation.

In the contemporary global economy, asset prices are much more sensitive to interest rate adjustments than consumer prices. The present value of assets is determined by expected flows of future returns, which are in turn estimated using today’s interest rates: a five-year bond that pays a fixed 2 percent rate of interest would sell at a lower price than it was purchased for following interest rate hikes. In this way, the prices of fixed income assets and market interest rates are inversely related: tighter monetary policy necessarily results in lower asset values …

The recent turmoil in financial markets has shown that the main tool central banks use to try to achieve price stability can destabilize the financial system. Monetary policy makers need to recognize that capitalist economies have two price systems which need different medicines. To marry modern-day price and financial stability requires greater coordination between fiscal, monetary, and wider industrial policy. Some examples of this could be seen in the response to the Covid-19 pandemic, where policies created fiscal space for massive investment in health care and the maintenance of jobs, and liquidity to support particular sectors. This more imaginative policymaking now needs to be scaled up to meet the multiple crises facing today’s world.

Josh Ryan-Collins

As all students of economics know, time is limited. Given that, there have to be better ways to optimize its utilization than spending hours and hours working through or constructing irrelevant economic models. I rather recommend my students allocate some time to study great forerunners like Keynes and Minsky, helping them to construct better and more relevant economic models — models that really help us to explain and understand reality.

The deadly sin of statistical reification

from Lars Syll

People sometimes speak as if random variables “behave” in a certain way, as if they have a life of their own. Thus “X is normally distributed”, “W follows a gamma”, “The underlying distribution behind y is binomial”, and so on. To behave is to act, to be caused, to react. Somehow, it is thought, these distributions are causes. This is the Deadly Sin of Reification, perhaps caused by the beauty of the mathematics where, due to some mental abstraction, the equations undergo biogenesis. The behavior of these “random” creatures is expressed in language about “distributions.” We hear, “Many things are normally (gamma, Weibull, etc. etc.) distributed”, “Height is normally distributed”, “Y is binomial”, “Independent, identically distributed random variables”.

There is no such thing as a “true” distribution in any ontological sense. Examples abound. The temptation here is magical thinking. Strictly and without qualification, to say a thing is “distributed as” is to assume murky causes are at work, pushing variables this way and that knowing they are “part of” some mathematician’s probability distribution.

To say a thing “has” a distribution is false. The only thing we are privileged to say is things like this: “Give this-and-such set of premises, the probability X takes this value equals that”, where “that” is calculated via a probability implied by the premises … Probability is a matter of ascribable or quantifiable uncertainty, a logical relation between accepted premises and some specified proposition, and nothing more.

William Briggs

In econometrics one often gets the feeling that many of its practitioners think of it as a kind of automatic inferential machine: input data and out comes casual knowledge. Like pulling a rabbit from a hat. Great — but first you have to put the rabbit in the hat. And this is where assumptions about distributions and probabilities come into the picture.

Why MMT is needed

from Lars Syll

Mainstream economists do not believe that “countries that borrow in their own currency should not worry about government deficits because they can always create money to finance their debt.” Looking at the result from a survey, not a single economist agreed with that statement. If these economists had been right, we would see lots of governments running out of money in 2020 and 2021. After all, tax revenues collapsed, government spending was increased and accordingly public deficits and public debts skyrocketed. Surely, the Greek government, surpassing 200 percent of public debt-to-GDP in 2021, would be in for a repeat of the Euro crisis? Nothing of that kind happened. As we all know by now, a government cannot run out of its own money for technical reasons … In the Eurozone, all national governments made their payments on time — all of them. This needs to be explained.

The explanations of mainstream economists seem unconvincing. Krugman (2021), for instance, writes: “But is the Fed really financing the budget deficit? Not really. At a fundamental level, households are financing the deficit: the funds being borrowed by the government are coming out of the huge savings undertaken by families saving much of their income in an environment where much of their usual consumption hasn’t felt safe.” The problem with this is that obviously the Fed does not borrow household savings (or rather saving, since this is about flows). It sells sovereign securities to banks only.

Dirk Ehnts

Few issues in politics and economics are nowadays more discussed — and less understood — than public debt.

The econometric dream-world

from Lars Syll

Trygve Haavelmo — with the completion (in 1958) of the twenty-fifth volume of Econometrica — assessed the role of econometrics in the advancement of economics, and although mainly positive of the “repair work” and “clearing-up work” done, he also found some grounds for despair:

We have found certain general principles which would seem to make good sense. Essentially, these principles are based on the reasonable idea that, if an economic model is in fact “correct” or “true,” we can say something a priori about the way in which the data emerging from it must behave. We can say something, a priori, about whether it is theoretically possible to estimate the parameters involved. And we can decide, a priori, what the proper estimation procedure should be … But the concrete results of these efforts have often been a seemingly lower degree of accuracy of the would-be economic laws (i.e., larger residuals), or coefficients that seem a priori less reasonable than those obtained by using cruder or clearly inconsistent methods.

There is the possibility that the more stringent methods we have been striving to develop have actually opened our eyes to recognize a plain fact: viz., that the “laws” of economics are not very accurate in the sense of a close fit, and that we have been living in a dream-world of large but somewhat superficial or spurious correlations.

Another of the founding fathers of modern probabilistic econometrics, Ragnar Frisch, shared Haavelmo’s doubts on the applicability of econometrics:

Mainstream economics — slipping from the model of reality to the reality of the model

from Lars Syll

A couple of years ago, Paul Krugman had a piece up on his blog arguing that the ‘discipline of modelling’ is a sine qua non for tackling politically and emotionally charged economic issues:

In my experience, modeling is a helpful tool (among others) in avoiding that trap, in being self-aware when you’re starting to let your desired conclusions dictate your analysis. Why? Because when you try to write down a model, it often seems to lead some place you weren’t expecting or wanting to go. And if you catch yourself fiddling with the model to get something else out of it, that should set off a little alarm in your brain.

So when ‘modern’ mainstream economists use their models — standardly assuming rational expectations, Walrasian market clearing, unique equilibria, time invariance, linear separability and homogeneity of both inputs/outputs and technology, infinitely lived intertemporally optimizing representative agents with homothetic and identical preferences, etc. — and standardly ignoring complexity, diversity, uncertainty, coordination problems, non-market clearing prices, real aggregation problems, emergence, expectations formation, etc. — we are supposed to believe that this somehow helps them ‘to avoid motivated reasoning that validates what you want to hear.’

Yours truly is, to say the least, far from convinced. The alarm in my brain is that this,

Weekend read – Bayes theorem — what’s the big deal?

from Lars Syll

There’s nothing magical about Bayes’ theorem. It boils down to the truism that your belief is only as valid as its evidence. If you have good evidence, Bayes’ theorem can yield good results. If your evidence is flimsy, Bayes’ theorem won’t be of much use. Garbage in, garbage out.

The potential for Bayes abuse begins with your initial estimate of the probability of your belief, often called the “prior” …

In many cases, estimating the prior is just guesswork, allowing subjective factors to creep into your calculations. You might be guessing the probability of something that–unlike cancer—does not even exist, such as strings, multiverses, inflation or God. You might then cite dubious evidence to support your dubious belief. In this way, Bayes’ theorem can promote pseudoscience and superstition as well as reason.

Embedded in Bayes’ theorem is a moral message: If you aren’t scrupulous in seeking alternative explanations for your evidence, the evidence will just confirm what you already believe. Scientists often fail to heed this dictum, which helps explains why so many scientific claims turn out to be erroneous. Bayesians claim that their methods can help scientists overcome confirmation bias and produce more reliable results, but I have my doubts.

And as I mentioned above, some string and multiverse enthusiasts are embracing Bayesian analysis. Why? Because the enthusiasts are tired of hearing that string and multiverse theories are unfalsifiable and hence unscientific, and Bayes’ theorem allows them to present the theories in a more favorable light. In this case, Bayes’ theorem, far from counteracting confirmation bias, enables it.

John Horgan

One of yours truly’s favourite ‘problem situating lecture arguments’ against Bayesianism goes something like this:

Busting the natural rate of unemployment myth

from Lars Syll

Almost sixty years ago Milton Friedman wrote an (in)famous article arguing that (1) the natural rate of unemployment was independent of monetary policy and that (2) trying to keep the unemployment rate below the natural rate would only give rise to higher and higher inflation.

The hypothesis has always been controversial, and much theoretical and empirical work has questioned the real-world relevance of the idea that unemployment really is independent of monetary policy and that there is no long-run trade-off between inflation and unemployment.

My own view on the subject is that the natural rate hypothesis does not hold water simply because the relations it describes have never actually existed.

The only thing that amazes yours truly is that although this is pretty common knowledge,  so-called ‘New Keynesian’ macroeconomists still today use it — and its cousin the Phillips curve — as a fundamental building block in their models. Why? Because without it ‘New Keynesians’ have to give up their (again and again empirically falsified) neoclassical view of the long-run neutrality of money and the simplistic idea of inflation as an excess-demand phenomenon.

Paul Krugman — finally — admits he was wrong!

from Lars Syll

In a recent essay titled “What Economists (Including Me) Got Wrong About Globalization,” adapted from a forthcoming book on inequality, Krugman writes that he and other mainstream economists “missed a crucial part of the story” in failing to realize that globalization would lead to “hyperglobalization” and huge economic and social upheaval, particularly of the industrial middle class in America. And many of these working-class communities have been hit hard by Chinese competition, which economists made a “major mistake” in underestimating, Krugman says.

It was quite a “whoops” moment, considering all the ruined American communities and displaced millions of workers we’ve seen in the interim. And a newly humbled Krugman must consider an even more disturbing idea: Did he and other mainstream economists help put a protectionist populist, Donald Trump, in the White House with a lot of bad advice about free markets?

Michael Hirsh

Yours truly has for years been complaining about Krugman on this issue, so of course, it’s great that he finally admits he was wrong!

Another issue on which I’m having a beef with Krugman is his view that his hobbyhorse IS-LM interpretation of Keynes is fruitful and relevant for understanding monetary economies. It is time for Krugman to come out on this also and admit he has been wrong.

On the benefits — and dangers — of reading

from Lars Syll

As long as reading is for us the instigator whose magic keys have opened the door to those dwelling-places deep within us that we would not have known how to enter, its role in our lives is salutary.  It becomes dangerous, on the other hand, when, instead of awakening us to the personal life of the mind, reading tends to take its place, when the truth no longer appears to us as an ideal which we can realize only by the intimate progress of our own thought and the efforts of our heart, but as something material, deposited between the leaves of books like a honey fully prepared by others and which we need only take the trouble to reach down from the shelves of libraries and then sample passively in a perfect repose of mind and body.

On fighting inflation

from Lars Syll

Absolutely lovely! Comedian and television host Jon Stewart turns out to know much more about real-world economics than mainstream Harvard economist Larry Summers. Don’t know why, but watching this interview/debate makes yours truly come to think about a famous H. C. Andersen tale …

Mainstream economics — a vending machine view

from Lars Syll

The theory is a vending machine: you feed it input in certain prescribed forms for the desired output; it gurgitates for a while; then it drops out the sought-for representation, plonk, on the tray, fully formed, as Athena from the brain of Zeus. This image of the relation of theory to the models we use to represent the world is hard to fit with what we know of how science works.

When applying deductivist thinking to economics, economists usually set up ‘as if” models based on a set of tight axiomatic assumptions from which consistent and precise inferences are made. The beauty of this procedure is of course that if the axiomatic premises are true, the conclusions necessarily follow. The snag is that if the models are to be relevant, we also have to argue that their precision and rigour still hold when they are applied to real-world situations. They often don’t. When addressing real economies, the idealizations necessary for the deductivist machinery to work, simply don’t hold.

So how should we evaluate the search for ever greater precision and the concomitant arsenal of mathematical and formalist models? To a large extent, the answer hinges on what we want our models to perform and how we basically understand the world.

Getting causality into statistics

Lars Syll  

Because statistical analyses need a causal skeleton to connect to the world, causality is not extra-statistical but instead is a logical antecedent of real-world inferences. Claims of random or “ignorable” or “unbiased” sampling or allocation are justified by causal actions to block (“control”) unwanted causal effects on the sample patterns. Without such actions of causal blocking, independence can only be treated as a subjective exchangeability assumption whose justification requires detailed contextual information about absence of factors capable of causally influencing both selection (including selection for treatment) and outcomes. Otherwise it is essential to consider pathways for the causation of biases (nonrandom, systematic errors) and their interactions …

Probability is inadequate as a foundation for applied statistics, because competent statistical practice integrates logic, context, and probability into scientific inference and decision, using causal narratives to explain diverse data. Thus, given the absence of elaborated causality discussions in statistics textbooks and coursework, we should not be surprised at the widespread misuse and misinterpretation of statistical methods and results. This is why incorporation of causality into introductory statistics is needed as urgently as other far more modest yet equally resisted reforms involving shifts in labels and interpretations for P-values and interval estimates.

Sander Greenland

Causality can never be reduced to a question of statistics or probabilities unless

Econometric fictionalism

from Lars Syll

If you can’t devise an experiment that answers your question in a world where anything goes, then the odds of generating useful results with a modest budget and nonexperimental survey data seem pretty slim. The description of an ideal experiment also helps you formulate causal questions precisely. The mechanics of an ideal experiment highlight the forces you’d like to manipulate and the factors you’d like to hold constant.

Research questions that cannot be answered by any experiment are fundamentally unidentified questions.

One of the limitations of economics is the restricted possibility to perform experiments, forcing it to mainly rely on observational studies for knowledge of real-world economies.

But still — the idea of performing laboratory experiments holds a firm grip on our wish to discover (causal) relationships between economic ‘variables.’If we only could isolate and manipulate variables in controlled environments, we would probably find ourselves in a situation where we with greater ‘rigour’ and ‘precision’ could describe, predict, or explain economic happenings in terms of ‘structural’ causes, ‘parameter’ values of relevant variables, and economic ‘laws.’

Galileo Galilei’s experiments are often held as exemplary for how to perform experiments to learn something about the real world. Galileo’s heavy balls dropping from the tower of Pisa, confirmed that the distance an object falls is proportional to the square of time and that this law (empirical regularity) of falling bodies could be applicable outside a vacuum tube when e. g. air existence is negligible.

The Keynes-Tinbergen debate on econometrics

from Lars Syll

It is widely recognized but often tacitly neglected that all statistical approaches have intrinsic limitations that affect the degree to which they are applicable to particular contexts … John Maynard Keynes was perhaps the first to provide a concise and comprehensive summation of the key issues in his critique of Jan Tinbergen’s book Statistical Testing of Business Cycle Theories …

Keynes’s intervention has, of course, become the basis of the “Tinbergen debate” and is a touchstone whenever historically or philosophically informed methodological discussion of econometrics is undertaken. It has remained the case, however, that Keynes’s concerns with the “logical issues” regarding the “conditions which the economic material must satisfy” still
gain little attention in theory and practice.

Muhammad Ali Nasir & Jamie Morgan

Mainstream economists often hold the view that Keynes’ criticism of econometrics was the result of a sadly misinformed and misguided person who disliked and did not understand much of it.

This is, however, as Nasir and Morgan convincingly argue, nothing but a gross misapprehension.

To be careful and cautious is not the same as to dislike. Keynes did not misunderstand the crucial issues at stake in the development of econometrics. Quite the contrary. He knew them all too well — and was not satisfied with the validity and philosophical underpinning of the assumptions made for applying its methods.

Keynes’ critique of the “logical issues” regarding the conditions that have to be satisfied if we are going to be able to apply econometric methods, is still valid and unanswered in the sense that the problems he pointed at are still with us today and largely unsolved. Ignoring them — the most common practice among applied econometricians — is not to solve them.

Economics as religion

from Lars Syll

Contrary to the tenets of orthodox economists, contemporary research suggests that, rather than seeking always to maximise our personal gain, humans still remain reasonably altruistic and selfless. Nor is it clear that the endless accumulation of wealth always makes us happier. And when we do make decisions, especially those to do with matters of principle, we seem not to engage in the sort of rational “utility-maximizing” calculus that orthodox economic models take as a given. The truth is, in much of our daily life we don’t fit the model all that well.

For decades, neoliberal evangelists replied to such objections by saying it was incumbent on us all to adapt to the model, which was held to be immutable – one recalls Bill Clinton’s depiction of neoliberal globalisation, for instance, as a “force of nature”. And yet, in the wake of the 2008 financial crisis and the consequent recession, there has been a turn against globalisation across much of the west …

It would be tempting for anyone who belongs to the “expert” class, and to the priesthood of economics, to dismiss such behaviour as a clash between faith and facts, in which the facts are bound to win in the end. In truth, the clash was between two rival faiths – in effect, two distinct moral tales. So enamoured had the so-called experts become with their scientific authority that they blinded themselves to the fact that their own narrative of scientific progress was embedded in a moral tale. It happened to be a narrative that had a happy ending for those who told it, for it perpetuated the story of their own relatively comfortable position as the reward of life in a meritocratic society that blessed people for their skills and flexibility. That narrative made no room for the losers of this order, whose resentments were derided as being a reflection of their boorish and retrograde character – which is to say, their fundamental vice. The best this moral tale could offer everyone else was incremental adaptation to an order whose caste system had become calcified. For an audience yearning for a happy ending, this was bound to be a tale of woe.

The failure of this grand narrative is not, however, a reason for students of economics to dispense with narratives altogether. Narratives will remain an inescapable part of the human sciences for the simple reason that they are inescapable for humans. It’s funny that so few economists get this, because businesses do.

Yes indeed, one would think it self-evident that “the facts are bound to win in the end.” But still, mainstream economists seem to be impressed by the ‘rigor’ they bring to macroeconomics with their totally unreal New-Classical-New-Keynesian DSGE models and their rational expectations and microfoundations!

The difference between logic and science

from Lars Syll 

In mainstream economics, both logic and mathematics are used extensively. And most mainstream economists sure look upon themselves as “twice blessed.”

Is there any scientific ground for that blessedness? None whatsoever!

If scientific progress in economics lies in our ability to tell ‘better and better stories’ one would, of course, expect economics journals to be filled with articles supporting the stories with empirical evidence confirming the predictions. However, the journals still show a striking and embarrassing paucity of empirical studies that (try to) substantiate these predictive claims. Equally amazing is how little one has to say about the relationship between the model and real-world target systems. It is as though explicit discussion, argumentation, and justification on the subject aren’t considered to be required.

In mathematics and logic, the deductive-axiomatic method has worked just fine. But science is not mathematics or logic. Conflating those two domains of knowledge has been one of the most fundamental mistakes made in modern economics.