The pretence-of-knowledge syndrome

26 Jan, 2020 at 14:05 | Posted in Economics | 2 Comments

pretenceThe reaction of human beings to the truly unknown is fundamentally different from the way they deal with the risks associated with a known situation and environment … In realistic, real-time settings, both economic agents and researchers have a very limited understanding of the mechanisms at work … In trying to add a degree of complexity to the current core models, by bringing in aspects of the periphery, we are simultaneously making the rationality assumptions behind that core approach less plausible …

The challenges are big, but macroeconomists can no longer continue playing internal games … I suspect that whatever the solution ultimately is, we will accelerate our convergence to it, and reduce the damage we do along the transition, if we focus on reducing the extent of our pretense-of-knowledge syndrome.

Ricardo J. Caballero

Caballero’s article underlines — especially when it comes to forecasting and implementing economic policies  — that the future is inherently unknowable, and using statistics, econometrics, decision theory or game theory, does not in the least overcome this ontological fact.

Uncertainty is something that has to be addressed and not only assumed away. To overcome the feeling of hopelessness when confronting ‘unknown unknowns’, it is important — in economics in particular — to incorporate Keynes’s far-reaching and incisive analysis of induction and evidential weight in A Treatise on Probability (1921).

treatprobAccording 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.”

How strange that social scientists and mainstream economists, as a rule, 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 measurable quantities, one puts a blind eye to qualities and looks the other way.

So why do economists, companies and governments continue with the expensive, but obviously worthless, activity of trying to forecast/predict the future?

Some time ago yours truly was interviewed by a public radio journalist working on a series on Great Economic ThinkersWe were discussing the monumental failures of the predictions-and-forecasts-business. But — the journalist asked — if these cocksure economists with their “rigorous” and “precise” mathematical-statistical-econometric models are so wrong again and again — why do they persist wasting time on it?

In a discussion on uncertainty and the hopelessness of accurately modelling what will happen in the real world — in M. Szenberg’s Eminent Economists: Their Life Philosophies — Nobel laureate Kenneth Arrowcomes up with what is probably the most plausible reason:

It is my view that most individuals underestimate the uncertainty of the world. This is almost as true of economists and other specialists as it is of the lay public. To me our knowledge of the way things work, in society or in nature, comes trailing clouds of vagueness … Experience during World War II as a weather forecaster added the news that the natural world as also unpredictable. cloudsAn incident illustrates both uncer-tainty and the unwilling-ness to entertain it. Some of my colleagues had the responsi-bility of preparing long-range weather forecasts, i.e., for the following month. The statisticians among us subjected these forecasts to verification and found they differed in no way from chance. The forecasters themselves were convinced and requested that the forecasts be discontinued. The reply read approximately like this: ‘The Commanding General is well aware that the forecasts are no good. However, he needs them for planning purposes.’

Uncertainty in economics

25 Jan, 2020 at 16:42 | Posted in Economics | 3 Comments

kadeNot accounting for uncertainty may result in severe confusion about what we do indeed understand about the economy. In the financial crisis of 2007/2008 the demon has lashed out at this ignorance and challenged the credibility of the whole economic community by laying bare economists’ incapability to prevent the crisis …

Economics itself cannot be regarded a purely analytical science. It has the amazing and exciting property of shaping the object of its own analysis. This feature clearly distinguishes it from physics, chemistry, archaeology and many other sciences. While biologists, chemists, engineers, physicists and many more are very able to transform whole societies by their discoveries and inventions — like Penicillin or the internet — the laws of nature they study remain unaffected by these inventions. In economic, this constancy of the object under study just does not exist.

The financial crisis of 2007-2008 hit most laymen and economists with surprise. What was it that went wrong with our macroeconomic models, since they obviously did not foresee the collapse or even made it conceivable?

There are many who have ventured to answer that question. And they have come up with a variety of answers, ranging from the exaggerated mathematization of economics, to irrational and corrupt politicians.

0But the root of our problem goes much deeper. It ultimately goes back to how we look upon the data we are handling. In ‘modern’ macroeconomics — Dynamic Stochastic General Equilibrium, New Synthesis, New Classical and New ‘Keynesian’ — variables are treated as if drawn from a known “data-generating process” that unfolds over time and on which we therefore have access to heaps of historical time-series. If we do not assume that we know the ‘data-generating process’ – if we do not have the ‘true’ model – the whole edifice collapses. And of course it has to. I mean, who honestly believes that we should have access to this mythical Holy Grail, the data-generating process?

‘Modern’ macroeconomics obviously did not anticipate the enormity of the problems that unregulated ‘efficient’ financial markets created. Why? Because it builds on the myth of us knowing the ‘data-generating process’ and that we can describe the variables of our evolving economies as drawn from an urn containing stochastic probability functions with known means and variances.

4273570080_b188a92980This is like saying that you are going on a holiday-trip and that you know that the chance the weather being sunny is at least 30%, and that this is enough for you to decide on bringing along your sunglasses or not. You are supposed to be able to calculate the expected utility based on the given probability of sunny weather and make a simple decision of either-or. Uncertainty is reduced to risk.

But as Keynes convincingly argued in his monumental Treatise on Probability (1921), this is not always possible. Often we simply do not know. According to one model the chance of sunny weather is perhaps somewhere around 10% and according to another – equally good – model the chance is perhaps somewhere around 40%. We cannot put exact numbers on these assessments. We cannot calculate means and variances. There are no given probability distributions that we can appeal to.

In the end this is what it all boils down to. We all know that many activities, relations, processes and events are of the Keynesian uncertainty-type. The data do not unequivocally single out one decision as the only ‘rational’ one. Neither the economist, nor the deciding individual, can fully pre-specify how people will decide when facing uncertainties and ambiguities that are ontological facts of the way the world works.

wrongrightSome macroeconomists, however, still want to be able to use their hammer. So they decide to pretend that the world looks like a nail, and pretend that uncertainty can be reduced to risk. So they construct their mathematical models on that assumption. The result: financial crises and economic havoc.

How much better — how much bigger chance that we do not lull us into the comforting thought that we know everything and that everything is measurable and we have everything under control — if instead we could just admit that we often simply do not know, and that we have to live with that uncertainty as well as it goes.

Fooling people into believing that one can cope with an unknown economic future in a way similar to playing at the roulette wheels, is a sure recipe for only one thing — economic disaster.

Modell och verklighet i nationalekonomi

24 Jan, 2020 at 17:21 | Posted in Economics | Leave a comment

Nationalekonomi är mer än någon annan samhällsvetenskap modellorienterad. Det finns många skäl till detta — ämnets historia, ideal hämtade från naturvetenskapen, universalitetsanpråk, viljan att förklara så mycket som möjligt med så lite som möjligt, rigör, precision med mera.

Tillvägagångssättet är i grunden analytiskt — helheten bryts ned i sina beståndsdelar så att det blir möjligt att förklara (reducera) aggregatet (makro) som ett resultat av interaktion mellan delarna (mikro).
ec modMainstreamekonomer baserar i regel sina modeller på ett antal kärnantaganden (CA) — som i grunden beskriver aktörer som ‘rationella’ — samt ett antal auxiliära antaganden (AA). Tillsammans utgör (CA) och (AA) vad vi skulle kunna kalla ’basmodellen’ (M) för alla mainstreammodeller. Baserat på dessa två uppsättningar av antaganden försöker man förklara och predicera både individuella (mikro) och samhälleliga fenomen (makro).

Kärnantagandena består typiskt av:
CA1 Fullständighet – den rationella aktören förmår alltid jämföra olika alternativ och bestämma vilket hon föredrar
CA2 Transitivitet – om aktören föredrar A framför B, och B framför C, måste hon föredra A framför C
CA3 Icke-mättnad — mer är alltid bättre än mindre
CA4 Maximering av förväntad nytta – i situationer känneteckade av risk maximerar aktören alltid den förväntade nyttan
CA5 Konsistenta ekonomiska jämvikter – olika aktörers handlande är konsistenta och interaktionen dem emellan resulterar i en jämvikt

När man beskriver aktörer som rationella i de här modellerna avser man instrumentell rationalitet, som innebär att aktörer förutsätts välja alternativ som har de bästa konsekvenserna utifrån deras givna preferenser. Hur dessa givna preferenser har uppkommit uppfattas i regel ligga utanför rationalitetsbegreppets ’omfång’ och därför inte heller utgör en del av den ekonomiska teorin som sådan.

Bilden man får av kärnantagandena (’rationella val’) är en rationell aktör med starka kognitiva kapaciteter, som vet vad hon vill, noga överväger sina alternativ och givet sina preferenser väljer vad hon tror har de bästa konsekvenserna för henne. Vägandes de olika alternativen mot varandra gör aktören ett konsistent, rationellt val och agerar utifrån detta.

De auxiliära antagandena (AA) specificerar rums- och tidmässigt vad för typ av interaktion som kan äga rum mellan ‘rationella’ aktörer. Antagandena ger ofta svar på frågor som:
AA1 vilka är aktörerna och var och när interagerar de
AA2 vilka är deras mål och aspirationer
AA3 vilka intressen har de
AA4 vilka är deras förväntningar
AA5 vad för slags handlingsutrymme har de
AA6 vilket slags överenskommelser kan de ingå
AA7 hur mycket och vad för slags information besitter de
AA8 hur interagerar deras handlingar med varandra

Så ‘basmodellen’ för alla mainstream-modeller består av en generell bestämning av vad som (axiomatiskt) utgör optimerande rationella aktörer (CA) samt en mer specifik beskrivning (AA) av i vad för slags situationer som dessa aktörer agerar (vilket innebär att AA fungerar som en restriktion som bestämmer den tilltänkta applikationsdomänen för CA och de därur deduktivt härledda teoremen). Listan över antaganden kan aldrig bli fullständig eftersom det alltid också förekommer ospecificerade ’bakgrundsantaganden’ och opåtalade utelämnanden (typ transktionskostnader, slutningar, o d, ofta baserat på något slags negligerbarhets- och applikationsöverväganden). Förhoppningen är att denna ’tunna’ uppsättning antaganden ska vara tillräcklig för att förklara och predicera ’fylliga’ fenomen i den verkliga, komplexa världen.

Continue Reading Modell och verklighet i nationalekonomi…

The intellectual regress of macroeconomics

21 Jan, 2020 at 17:45 | Posted in Economics | 1 Comment

Real business cycle theory — RBC — is one of the theories that has put macroeconomics on a path of intellectual regress for three decades now. And although there are many kinds of useless ‘post-real’ economics held in high regard within mainstream economics establishment today, few — if any — are less deserved than real business cycle theory.

The future is not reducible to a known set of prospects. It is not like sitting at the roulette table and calculating what the future outcomes of spinning the wheel will be. So instead of — as RBC economists do — assuming calibration and rational expectations to be right, one ought to confront the hypothesis with the available evidence. It is not enough to construct models. Anyone can construct models. To be seriously interesting, models have to come with an aim. They have to have an intended use. If the intention of calibration and rational expectations is to help us explain real economies, it has to be evaluated from that perspective. A model or hypothesis without specific applicability is not really deserving of our interest.

Without strong evidence, all kinds of absurd claims and nonsense may pretend to be science. We have to demand more of a justification than rather watered-down versions of ‘anything goes’ when it comes to rationality postulates. If one proposes rational expectations one also has to support its underlying assumptions. None is given by RBC economists, which makes it rather puzzling how rational expectations has become the standard modelling assumption made in much of modern macroeconomics. Perhaps the reason is that economists often mistake mathematical beauty for truth.

In the hands of Lucas, Prescott and Sargent, rational expectations have been transformed from an — in-principle — testable hypothesis to an irrefutable proposition. Believing in a set of irrefutable propositions may be comfortable – like religious convictions or ideological dogmas – but it is not science.

So where does this all lead us? What is the trouble ahead for economics? Putting a sticky-price DSGE lipstick on the RBC pig sure won’t do. Neither will — as Paul Romer noticed  — just looking the other way and pretend it’s raining:

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.

Alternatives to mainstream economics

20 Jan, 2020 at 09:03 | Posted in Economics | 3 Comments


Quinn Slobodian and the birth of neoliberalism

18 Jan, 2020 at 13:55 | Posted in Economics, Politics & Society | 1 Comment


It is a measure of the success of this fascinating, innovative history that it forces the question: after Slobodian’s reinterpretation, where does the critique of neoliberalism stand?

First and foremost, Slobodian has underlined the profound conservatism of the first generation of neoliberals and their fundamental hostility to democracy. What he has exposed, furthermore, is their deep commitment to empire as a restraint on the nation state. Notably, in the case of Wilhelm Röpke, this was reinforced by deep-seated anti-black racism. Throughout the 1960s Röpke was active on behalf of South Africa and Rhodesia in defense of what he saw as the last bastions of white civilization in the developing world. As late as the 1980s, members of the Mont Pèlerin Society argued that the white minority in South Africa could best be defended by weighting the voting system by the proportion of taxes paid. If this was liberalism it was not so much neo- as paleo-.

Adam Tooze

On the impossibility of efficient markets

17 Jan, 2020 at 18:46 | Posted in Economics | 6 Comments

In general the price system does not reveal all the information about “the true value” of the risky asset …

tumblr_n6vk0tAVwh1rlnhn7o1_500The only way informed traders can earn a return on their activity of information gathering, is if they can use their information to take positions in the market which are “better” than the positions of uninformed traders. “Efficient Markets” theorists have claimed that “at any time prices fully reflect all available information” … If this were so then informed traders could not earn a return on their information.

When the efficient markets hypothesis is true and information is costly, competitive markets break down … As soon as the assumptions of the conventional perfect capital markets model are modified to allow even a slight amount of information imperfection and a slight cost of information, the traditional theory becomes untenable. There cannot be as many securities as states of nature. For if there were, competitive equilibrium would not exist …

Because information is costly, prices cannot perfectly reflect the information which is available, since if it did, those who spent resources to obtain it would receive no compensation. There is a fundamental conflict between the efficiency with which markets spread information and the incentives to acquire information.

Sanford Grossman & Joseph Stiglitz

Here (in Swedish) is my own take on the paradox.

Chicago economics — only for Gods and Idiots

15 Jan, 2020 at 14:27 | Posted in Economics | 2 Comments

4703325-2If I ask myself what I could legitimately assume a person to have rational expectations about, the technical answer would be, I think, about the realization of a stationary stochastic process, such as the outcome of the toss of a coin or anything that can be modeled as the outcome of a random process that is stationary. I don’t think that the economic implications of the outbreak of World war II were regarded by most people as the realization of a stationary stochastic process. In that case, the concept of rational expectations does not make any sense. Similarly, the major innovations cannot be thought of as the outcome of a random process. In that case the probability calculus does not apply.

Robert Solow

‘Modern’ macroeconomic theories are as a rule founded on the assumption of rational expectations — where the world evolves in accordance with fully predetermined models where uncertainty has been reduced to stochastic risk describable by some probabilistic distribution.

The tiny little problem that there is no hard empirical evidence that verifies these models — cf. Michael Lovell (1986) and Nikolay Gertchev (2007) — usually doesn’t bother its protagonists too much. Rational expectations überpriest Thomas Sargent has the following to say on the epistemological status of the rational expectations hypothesis:

Partly because it focuses on outcomes and does not pretend to have behavioral content, the hypothesis of rational epectations has proved to be a powerful tool for making precise statements about complicated dynamic economic systems.

Precise, yes, in the celestial world of models. But relevant and realistic? I’ll be dipped!

And a few years later, when asked if he thought “that differences among people’s models are important aspects of macroeconomic policy debates”, Sargent replied:

The fact is you simply cannot talk about their differences within the typical rational expectations model. There is a communism of models. All agents within the model, the econometricians, and God share the same model.

Building models on rational expectations either means we are Gods or Idiots. Most of us know we are neither. So, Gods and Idiots may share Sargent’s and Lucas’s models, but they certainly aren’t my models.

Economics — too important to be left to economists

11 Jan, 2020 at 14:25 | Posted in Economics | 2 Comments

aaBad economics underpinned the grand giveaways to the rich and the squeezing of welfare programs, sold the idea that the state is impotent and corrupt and the poor are lazy, and paved the way to the current stalemate of exploding inequality and angry inertia. Blinkered economics told us trade is good for everyone, and faster growth is everywhere. Blind economics missed the explosion in inequality all over the world, the increasing social fragmentation that came with it, and the impending environmental disaster, delaying action, perhaps irrevocably.

Two of last year’s ‘Nobel Prize’ winners in economics, Esther Duflo and Abhijit Banerjee, are back with a follow-up to their 2011 book Poor Economics. In the new book — Good Economics for Hard Times — they set out to show that although few people nowadays trust economists, there is a way to “make economics great again.” What has undermined the general public’s trust in economists is bad economics, and there has been, and still is, plenty of it (the authors take trade liberalisation, growth theory, migration, inequality and climate change as poignant examples.) The alternative, good, economics is — as was argued already in the earlier book — what results when economists work more like plumbers and “solve problems with a combination intuition grounded in science, some guesswork aided by experience, and a bunch of pure trial and error.”

Although yours truly agrees with most of the picture the authors give of present-day bad (mainstream) economics, I’m less convinced of their alternative.

Duflo and Banerjee think that economics should be based on evidence from randomised experiments and field studies. But to give up on ‘big ideas’ like political economy and institutional reform and instead go for solving more manageable problems the way plumbers do, is in my view not sufficient to move economics forward and make it a relevant and realist science. A plumber can fix minor leaks in your system, but if the whole system is rotten, something more than good old fashion plumbing is needed. The big social and economic problems we face today are not going to be solved by plumbers performing RCTs. We need to dig deeper than plumbers and make sure we get at the deep causal mechanisms behind the present stagnation of capitalism and the climate catastrophe it has landed us in.

There is also a rather disturbing kind of scientific naïveté in the Duflo-Banerjee approach to combatting socio-economic and environmental problems. The way they present their whole endeavour smacks of not so little ‘scientism’ where fighting problems becomes a question of applying ‘objective’ quantitative ‘techniques.’ But that can’t be the right way! Fighting problems like poverty and inequality is basically a question of changing the structures and institutions of our economies and societies.

Does it — really — take a model to beat a model?

10 Jan, 2020 at 11:05 | Posted in Economics | 3 Comments

A critique yours truly sometimes encounters is that as long as I cannot come up with some own alternative model to the failing mainstream models, I shouldn’t expect people to pay attention.

This is, however, to totally and utterly misunderstand the role of philosophy and methodology of economics!

As John Locke wrote in An Essay Concerning Human Understanding:

19557-004-21162361The Commonwealth of Learning is not at this time without Master-Builders, whose mighty Designs, in advancing the Sciences, will leave lasting Monuments to the Admiration of Posterity; But every one must not hope to be a Boyle, or a Sydenham; and in an Age that produces such Masters, as the Great-Huygenius, and the incomparable Mr. Newton, with some other of that Strain; ’tis Ambition enough to be employed as an Under-Labourer in clearing Ground a little, and removing some of the Rubbish, that lies in the way to Knowledge.

That’s what philosophy and methodology can contribute to economics — clearing obstacles to science by clarifying limits and consequences of choosing specific modelling strategies, assumptions, and ontologies.

unnameadIt takes a model to beat a model has to be one of the stupider things, in a pretty crowded field, to come out of economics. … I don’t get it. If a model is demonstrably wrong, that should surely be sufficient for rejection. I’m thinking of bridge engineers: ‘look I know they keep falling down but I’m gonna keep building em like this until you come up with a better way, OK?’

Jo Michell

Why all the fuzz about trade?

9 Jan, 2020 at 22:21 | Posted in Economics | 5 Comments

imagesThe share of US expenditure on imports is smaller than in most other countries. To a large extent, this reflects the fact that for a large country like the United States, a significant fraction of trade occurs intra- rather than internationally. This basic observation implies that that the welfare gains from international trade in the United States are smaller than in most other countries. Although magnitudes vary greatly depending on how one infers the shape of the US demand for foreign factor services, the estimates of gains from trade for the US economy that we review range from 2 to 8 percent of GDP.

Though such gains are nothing to spit at—they are an order of magnitude larger than the estimated gains from eliminating business cycle fluctuations—they may appear surprisingly small to some.

Arnaud Costinot & Andrés Rodríguez-Clare

And then we haven’t even started talking about the asymmetric distribution of pains and gains of international trade …

Is economics — really — predictable?

9 Jan, 2020 at 14:54 | Posted in Economics | 2 Comments

oskarAs Oskar Morgenstern noted already back in his 1928 classic Wirtschaftsprognose: Eine Untersuchung ihrer Voraussetzungen und Möglichkeiten, economic predictions and forecasts amount to little more than intelligent guessing.

Making forecasts and predictions obviously isn’t a trivial or costless activity, so why then go on with it?

The problems that economists encounter when trying to predict the future really underlines how important it is for social sciences to incorporate Keynes’s far-reaching and incisive analysis of induction and evidential weight in his seminal A Treatise on Probability (1921).

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. treatprobTo 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.”

How strange that social scientists and mainstream economists, as a rule, 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 measurable quantities, one puts a blind eye to qualities and looks the other way.

So why do companies, governments, and central banks, continue with this more or less expensive, but obviously worthless, activity?

A part of the answer concerns ideology and apologetics. Forecasting is a non-negligible part of the labour market for (mainstream) economists, and so, of course, those in the business do not want to admit that they are occupied with worthless things (not to mention how hard it would be to sell the product with that kind of frank truthfulness). Governments, the finance sector and (central) banks also want to give the impression to customers and voters that they, so to say, have the situation under control (telling people that next years x will be 3.048 % makes wonders in that respect). Why else would anyone want to pay them or vote for them? These are sure not glamorous aspects of economics as a science, but as a scientist, it would be unforgivably dishonest to pretend that economics doesn’t also perform an ideological function in society.

On the use of mathematics in economics

6 Jan, 2020 at 13:54 | Posted in Economics | 4 Comments

Balliol Croft, Cambridge
27. ii. 06
My dear Bowley,

I had a growing feeling in the later years of my work at the subject that a good mathematical theorem dealing with economic hypotheses was very unlikely to be good economics: and I went more and more on the rules — (1) Use mathematics as a short-hand language, rather than as an engine of inquiry. (2) Keep to them till you have done. (3) Translate into English. (4) Then illustrate by examples that are important in real life. (5) Burn the mathematics. (6) If you can’t succeed in 4, burn 3. This last I did often …

Your emptyhandedly,

Alfred Marshall

As social researchers, we should never equate science with mathematics and statistical calculation. All science entail human judgement, and using mathematical and statistical models don’t relieve us of that necessity. They are no substitutes for doing real science.

amathMathematics is one valuable tool among other valuable tools for understanding and explaining things in economics.

What is, however, totally wrong, are the utterly simplistic beliefs that

• “math is the only valid tool”

• “math is always and everywhere self-evidently applicable”

• “math is all that really counts”

• “if it’s not in math, it’s not really economics”

And in case you — still — think this critique is some odd outcome of heterodox idiosyncrasy, well, maybe you should think twice …

einsteinIn mathematics, the deductive-axiomatic method has worked just fine. But science is not mathematics. Conflating those two domains of knowledge has been one of the most fundamental mistakes made in modern economics. Applying it to real-world open systems immediately proves it to be excessively narrow and hopelessly irrelevant. Both the confirmatory and explanatory ilk of hypothetico-deductive reasoning fails since there is no way you can relevantly analyze confirmation or explanation as a purely logical relation between hypothesis and evidence or between law-like rules and explananda. In science, we argue and try to substantiate our beliefs and hypotheses with reliable evidence. Propositional and predicate deductive logic, on the other hand, is not about reliability, but the validity of conclusions given that premises are true.

Crasht im Jahr 2023 die Weltwirtschaft?

5 Jan, 2020 at 14:53 | Posted in Economics | Leave a comment


What went wrong with economics

4 Jan, 2020 at 16:28 | Posted in Economics | 13 Comments

To be ‘analytical’ is something most people find recommendable. The word ‘analytical’ has a positive connotation. Scientists think deeper than most other people because they use ‘analytical’ methods. In dictionaries, ‘analysis’ is usually defined as having to do with “breaking something down.”

anBut that’s not the whole picture. As used in science, analysis usually means something more specific. It means to separate a problem into its constituent elements so to reduce complex — and often complicated — wholes into smaller (simpler) and more manageable parts. You take the whole and break it down (decompose) into its separate parts. Looking at the parts separately one at a time you are supposed to gain a better understanding of how these parts operate and work. Built on that more or less ‘atomistic’ knowledge you are then supposed to be able to predict and explain the behaviour of the complex and complicated whole.

In economics, that means you take the economic system and divide it into its separate parts, analyse these parts one at a time, and then after analysing the parts separately, you put the pieces together.

The ‘analytical’ approach is typically used in economic modelling, where you start with a simple model with few isolated and idealized variables. By ‘successive approximations,’ you then add more and more variables and finally get a ‘true’ model of the whole.

This may sound like a convincing and good scientific approach.

But there is a snag!

The procedure only really works when you have a machine-like whole/system/economy where the parts appear in fixed and stable configurations. And if there is anything we know about reality, it is that it is not a machine! The world we live in is not a ‘closed’ system. On the contrary. It is an essentially ‘open’ system. Things are uncertain, relational, interdependent, complex, and ever-changing.

Without assuming that the underlying structure of the economy that you try to analyze remains stable/invariant/constant, there is no chance the equations of the model remain constant. That’s the very rationale why economists use (often only implicitly) the assumption of ceteris paribus. But — nota bene — this can only be a hypothesis. You have to argue the case. If you cannot supply any sustainable justifications or warrants for the adequacy of making that assumption, then the whole analytical economic project becomes pointless non-informative nonsense. Not only have we to assume that we can shield off variables from each other analytically (external closure). We also have to assume that each and every variable themselves are amenable to be understood as stable and regularity producing machines (internal closure). Which, of course, we know is as a rule not possible. Some things, relations, and structures are not analytically graspable. Trying to analyse parenthood, marriage, employment, etc, piece by piece doesn’t make sense. To be a chieftain, a capital-owner, or a slave is not an individual property of an individual. It can come about only when individuals are integral parts of certain social structures and positions. Social relations and contexts cannot be reduced to individual phenomena. A cheque presupposes a banking system and being a tribe-member presupposes a tribe.  Not taking account of this in their ‘analytical’ approach, economic ‘analysis’ becomes uninformative nonsense.

Using the ‘analytical’ method in social sciences means that economists succumb to the fallacy of composition — the belief that the whole is nothing but the sum of its parts.  In society and in the economy this is arguably not the case. An adequate analysis of society and economy a fortiori cannot proceed by just adding up the acts and decisions of individuals. The whole is more than a sum of parts.

Mainstream economics is built on using the ‘analytical’ method. The models built with this method presuppose that social reality is ‘closed.’ Since social reality is known to be fundamentally ‘open,’ it is difficult to see how models of that kind can explain anything about what happens in such a universe. Postulating closed conditions to make models operational and then impute these closed conditions to society’s real structure is an unwarranted procedure that does not take necessary ontological considerations seriously.

In face of the kind of methodological individualism and rational choice theory that dominate mainstream economics we have to admit that even if knowing the aspirations and intentions of individuals are necessary prerequisites for giving explanations of social events, they are far from sufficient. Even the most elementary ‘rational’ actions in society presuppose the existence of social forms that it is not possible to reduce to the intentions of individuals. Here, the ‘analytical’ method fails again.

The overarching flaw with the ‘analytical’ economic approach using methodological individualism and rational choice theory is basically that they reduce social explanations to purportedly individual characteristics. But many of the characteristics and actions of the individual originate in and are made possible only through society and its relations. Society is not a Wittgensteinian ‘Tractatus-world’ characterized by atomistic states of affairs. Society is not reducible to individuals, since the social characteristics, forces, and actions of the individual are determined by pre-existing social structures and positions. Even though society is not a volitional individual, and the individual is not an entity given outside of society, the individual (actor) and the society (structure) have to be kept analytically distinct. They are tied together through the individual’s reproduction and transformation of already given social structures.

Since at least the marginal revolution in economics in the 1870s it has been an essential feature of economics to ‘analytically’ treat individuals as essentially independent and separate entities of action and decision. But, really, in such a complex, organic and evolutionary system as an economy, that kind of independence is a deeply unrealistic assumption to make. To simply assume that there is strict independence between the variables we try to analyze doesn’t help us the least if that hypothesis turns out to be unwarranted.

To be able to apply the ‘analytical’ approach, economists have to basically assume that the universe consists of ‘atoms’ that exercise their own separate and invariable effects in such a way that the whole consist of nothing but an addition of these separate atoms and their changes. These simplistic assumptions of isolation, atomicity, and additivity are, however, at odds with reality. In real-world settings, we know that the ever-changing contexts make it futile to search for knowledge by making such reductionist assumptions. Real-world individuals are not reducible to contentless atoms and so not susceptible to atomistic analysis. The world is not reducible to a set of atomistic ‘individuals’ and ‘states.’ How variable X works and influence real-world economies in situation A cannot simply be assumed to be understood or explained by looking at how X works in situation B. Knowledge of X probably does not tell us much if we do not take into consideration how it depends on Y and Z. It can never be legitimate just to assume that the world is ‘atomistic.’ Assuming real-world additivity cannot be the right thing to do if the things we have around us rather than being ‘atoms’ are ‘organic’ entities.

If we want to develop new and better economics we have to give up on the single-minded insistence on using a deductivist straitjacket methodology and the ‘analytical’ method. To focus scientific endeavours on proving things in models is a gross misapprehension of the purpose of economic theory. Deductivist models and ‘analytical’ methods disconnected from reality are not relevant to predict, explain or understand real-world economies.

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