Scientific advancement in economics

8 Jul, 2019 at 18:05 | Posted in Economics | 3 Comments


Minimal realism — much ado about nothing

8 Jul, 2019 at 17:40 | Posted in Theory of Science & Methodology | 3 Comments

ccTo generalise Mäki’s distinction between realism and realisticness, someone who believes that economic theories must or should include unrealistic assumptions is not necessarily a non-realist in the broader sense of philosophical realism: “A realist economist is permitted, indeed required, to use unrealistic assumptions in order to isolate what are believed to be the most essential features in a complex situation … To count as a minimal realist, an economist is required to believe that economic reality is unconstituted by his or her representations of it and that whatever truth value those representations have is independent of his or her or anybody else’s opinions of it” (Mäki 1994: 248).

Although Lawson would presumably not deny that orthodox economic theorists account as minimal realists in this sense, his concern is that orthodox economic theory is unrealistic in not representing the way things really are in that it does not refer factually and does not latch onto what is essential in the social domain … Lawson’s standpoint is that economic theory should strive for true explanations of social phenomena, hence Lawson is a methodological realist in this respect.

Duncan Hodge


8 Jul, 2019 at 10:49 | Posted in Economics | 2 Comments

Time is what prevents everything from happening at once. To simply assume that economic processes are ergodic and concentrate on ensemble averages — and hence in any relevant sense timeless — is not a sensible way for dealing with the kind of genuine uncertainty that permeates real-world economies.

Ergodicity and the all-important difference between time averages and ensemble averages are difficult concepts — so let me try to explain the meaning of these concepts by means of a couple of simple examples.

Let’s say you’re offered a gamble where on a roll of a fair die you will get €10  billion if you roll a six, and pay me €1 billion if you roll any other number.

Would you accept the gamble?

If you’re an economics student​ you probably would because that’s what you’re taught to be the only thing consistent with being rational. You would arrest the arrow of time by imagining six different “parallel universes” where the independent outcomes are the numbers from one to six, and then weight them using their stochastic probability distribution. Calculating the expected value of the gamble – the ensemble average – by averaging on all these weighted outcomes you would actually be a moron if you didn’t take the gamble (the expected value of the gamble being 5/6*€0 + 1/6*€10 billion = €1.67 billion)

If you’re not an economist you would probably trust your common sense and decline the offer, knowing that a large risk of bankrupting one’s economy is not a very rosy perspective for the future. Since you can’t really arrest or reverse the arrow of time, you know that once you have lost the €1 billion, it’s all over. The large likelihood that you go bust weights heavier than the 17% chance of you becoming enormously rich. By computing the time average – imagining one real universe where the six different but dependent outcomes occur consecutively – we would soon be aware of our assets disappearing, and a fortiori that it would be irrational to accept the gamble.

flipWhy is the difference between ensemble and time averages of such importance in economics? Well, basically, because when assuming the processes to be ergodic, ensemble and time averages are identical.

Assume we have a market with an asset priced at €100.​ Then imagine the price first goes up by 50% and then later falls by 50%. The ensemble average for this asset would be €100 – because we here envision two parallel universes (markets) where the asset price​ falls in one universe (market) with 50% to €50, and in another universe (market) it goes up with 50% to €150, giving an average of 100 € ((150+50)/2). The time average for this asset would be 75 € – because we here envision one universe (market) where the asset price first rises by 50% to €150 and then falls by 50% to €75 (0.5*150).

From the ensemble perspective nothing really, on average, happens. From the time perspective lots of things really, on average, happen. Assuming ergodicity there would have been no difference at all.

On a more economic-theoretical level, ​the difference between ensemble and time averages also highlights the problems concerning the neoclassical theory of expected utility.

When applied to the neoclassical theory of expected utility, one thinks in terms of “parallel universe” and asks what is the expected return of an investment, calculated as an average over the “parallel universe”? In our coin tossing example, it is as if one supposes that various “I” are tossing a coin and that the loss of many of them will be offset by the huge profits one of these “I” does. But this ensemble average does not work for an individual, for whom a time average better reflects the experience made in the “non-parallel universe” in which we live.

Time averages give​ a more realistic answer, where one thinks in terms of the only universe we actually live in and ask what is the expected return of an investment, calculated as an average over time.

onewaySince we cannot go back in time – entropy and the arrow of time make this impossible – and the bankruptcy option is always at hand (extreme events and “black swans” are always possible) we have nothing to gain from thinking in terms of ensembles.

Actual events follow a fixed pattern of time, where events are often linked in a multiplicative process (as e. g. investment returns with “compound interest”) which is basically non-ergodic.

Instead of arbitrarily assuming that people have a certain type of utility function – as in the neoclassical theory – time average considerations show that we can obtain a less arbitrary and more accurate picture of real people’s decisions and actions by basically assuming that time is irreversible. When your assets are gone, they are gone. The fact that in a parallel universe it could conceivably have been refilled, is​ of little comfort to those who live in the one and only possible world that we call the real world.

Our coin toss example can be applied to more traditional economic issues. If we think of an investor, we can basically describe his situation in terms of our coin toss. What fraction of his assets should an investor – who is about to make a large number of repeated investments – bet on his feeling that he can better evaluate an investment (p = 0.6) than the market (p = 0.5)? The greater the fraction, the greater is the leverage. But also – the greater is the risk. Letting p be the probability that his investment valuation is correct and (1 – p) is the probability that the market’s valuation is correct, it means that he optimizes the rate of growth on his investments by investing a fraction of his assets that is equal to the difference in the probability that he will “win” or “lose”. This means that he at each investment opportunity (according to the so-called Kelly criterion) is to invest the fraction of  0.6 – (1 – 0.6), i.e. about 20% of his assets (and the optimal average growth rate of investment can be shown to be about 2% (0.6 log (1.2) + 0.4 log (0.8))).

Time average considerations show that because we cannot go back in time, we should not take excessive risks. High leverage increases the risk of bankruptcy. This should also be a warning for the financial world, where the constant quest for greater and greater leverage – and risks – creates extensive and recurrent systemic crises. A more appropriate level of risk-taking is a necessary ingredient in policy to come to curb excessive risk-taking​.

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 are not necessarily those that will rule the future.

Irreversibility can no longer be identified with a mere appearance​ that would disappear if we had perfect knowledge … Figuratively speaking, matter at equilibrium, with no arrow of time, is ‘blind,’ but with the arrow of time, it begins to ‘see’ … The claim that the arrow of time is ‘only phenomenological​,’ or subjective, is therefore absurd. We are actually the children of the arrow of time, of evolution, not its progenitors.

Ilya Prigogine

Game theorists — people carried away by fictions

6 Jul, 2019 at 17:58 | Posted in Economics | 4 Comments

Applied game theory is a theory of real-world facts, where we use game theoretical definitions, axioms, theorems and (try to) test if real-world phenomena ‘satisfy’ the axioms and the inferences made from them. When confronted with the real world we can (hopefully) judge if game theory really tells us if things are as postulated by theory.

like-all-of-mathematics-game-theory-is-a-tautology-whose-conclusions-are-true-because-they-are-quote-1But there is also an influential group of game theoreticians that think that game theory is nothing but pure theory, an axiomatic-mathematical scientific theory that presents a set of axioms that people have to ‘satisfy’ by definition to count as ‘rational.’ Instead of confronting the theory with real-world phenomena it becomes a simple matter of definition if real-world phenomena are to count as signs of ‘rationality.’

This makes for ‘rigorous’ and ‘precise’ conclusions — but never about the real world. Pure game theory does not give us any information at all about the real world. It gives us absolutely irrefutable knowledge — but only since the knowledge is purely definitional.

Mathematical theorems are tautologies. They cannot be false because they do not say anything substantive. They merely spell out the implications of how things have been​ defined. The basic propositions of game theory have precisely the same character.

Ken Binmore

Pure game theorists, like Ken Binmore, give us analytical truths — truths by definition. That is great — from a mathematical and formal logical point of view. In science, however, it is rather uninteresting and totally uninformative! Even if pure game theory gives us ‘logical’ truths, that is not what we are looking for as scientists. We are interested in finding truths that give us new information and knowledge of the world in which we live.

Scientific theories are theories that ‘refer’ to the real-world, where axioms and definitions do not take us very far. To be of interest for an economist or social scientist that wants to understand, explain, or predict real-world phenomena, the pure theory has to be ‘interpreted’ — it has to be ‘applied’ theory. A ‘pure’ game theory that does not go beyond proving theorems and conditional ‘if-then’ statements — and do not make assertions and put forward hypotheses about real-world individuals and institutions — is of little consequence for anyone wanting to use theories to better understand, explain or predict real-world phenomena.

Pure game theory has no empirical content whatsoever. And it certainly has no relevance whatsoever to a scientific endeavour of expanding real-world knowledge.

Daniel Hausmann once famously criticized Paul Samuelson’s overlapping generations models for having been “carried away by fictions.” As far as I can see, the same goes for game theorists and their models.

Das Leben der Anderen

6 Jul, 2019 at 15:36 | Posted in Politics & Society | Leave a comment

Die Gezi-Proteste, an denen sich 2013 einige Millionen beteiligt hatten, fielen der Regierung nun wieder ein, als sie vor den Wahlen plötzlich einen Feind brauchte. 16 Oppositionelle, auch ich, stehen nun vor Gericht, lebenslang sollen wir in Haft … An der Spitze der Anklage steht Erdoğan …

turkDer Hauptangeklagte ist der Unternehmer und NGO-Pionier Osman Kavala. Auf der Rückreise von einem gemeinsamen Projekt mit dem Goethe-Institut wurde er festgenommen, seit gut 600 Tagen ist er inhaftiert. Ihm wird vorgeworfen, den Aufstand finanziert zu haben … Mir wird zur Last gelegt, ich hätte versucht, mit Kavala “alternative Medien” aufzubauen. Die Anklageschrift führt auch folgende “Vergehen” auf: Der Polizei wurden Blumen überreicht, und Slogans wurden auf Mauern gemalt …

Die wahre “Komödie” ist, dass die für den Lauschangriff verantwortlichen Staatsanwälte und der Polizeichef später selbst des “Umsturzversuchs” bezichtigt wurden und sich nun zum Teil als Flüchtlinge in Deutschland aufhalten. Ein Polizeichef, der das Schicksal des Mannes teilt, dessen Telefon er abhören ließ. Klingt das nicht filmreif? Wie der Plot des oscarprämierten Films Das Leben der Anderen?

Can Dündar

Living On My Own

6 Jul, 2019 at 15:11 | Posted in Varia | 1 Comment


[Film clip from the short film Lila]

What can we learn from economic models?

5 Jul, 2019 at 12:18 | Posted in Economics | 2 Comments

ektSome economic methodologists have lately been arguing that economic models may well be considered ‘minimal models’ that portray ‘credible worlds’ without having to care about things like similarity, isomorphism, simplified representationality or resemblance to the real world. These models are said to resemble ‘realistic novels’ that portray ‘possible worlds’. And sure: economists constructing and working with that kind of models learn things about what might happen in those ‘possible worlds’. But is that really the stuff real science is made of? I think not.

As long as one doesn’t come up with credible export warrants to real-world target systems and show how those models — often building on idealizations with known to be false assumptions — enhance our understanding or explanations about the real world, well, then they are just nothing more than just novels.  Showing that something is possible in a ‘possible world’ doesn’t give us a justified license to infer that it therefore also is possible in the real world. ‘The Great Gatsby’ is a wonderful novel, but if you truly want to learn about what is going on in the world of finance, I would recommend rather reading Minsky or Keynes and directly confront real-world finance.

Different models have different cognitive goals. Constructing models that aim for explanatory insights may not optimize the models for making (quantitative) predictions or deliver some kind of ‘understanding’ of what’s going on in the intended target system. All modelling in science have tradeoffs. There simply is no ‘best’ model. For one purpose in one context model A is ‘best’, for other purposes and contexts model B may be deemed ‘best’. Depending on the level of generality, abstraction, and depth, we come up with different models. But even so, I would argue that if we are looking for what I have called ‘adequate explanations’ (Ekonomisk teori och metod, 2005) it is not enough to just come up with ‘minimal’ or ‘credible world’ models.

The assumptions and descriptions we use in our modelling have to be true — or at least ‘harmlessly’ false — and give a sufficiently detailed characterization of the mechanisms and forces at work. Models in mainstream economics do nothing of the kind.

Coming up with models that show how things may possibly be explained is not what we are looking for. It is not enough. We want to have models that build on assumptions that are not in conflict with known facts and that show how things actually are to be explained. Our aspirations have to be more far-reaching than just constructing coherent and ‘credible’ models about ‘possible worlds’. We want to understand and explain ‘difference-making’ in the real world and not just in some made-up fantasy world. No matter how many mechanisms or coherent relations are represented in your model, it still has to be shown that these mechanisms and relations are at work and exist in society if we are to pay attention to the findings.

Science has to be something more than just more or less realistic ‘story-telling’ or ‘explanatory fictionalism’. One has to provide decisive empirical evidence that what can be inferred in a model also helps us to uncover what actually goes on in the real world. It is not enough to present students with epistemically informative insights about logically possible but non-existent general equilibrium models. It also, and more importantly, has to have a world-linking argumentation that shows how those models explain or teach us something about real-world economies. Failing to support models in that way, why should we care about them?

If we are not informed about what are the real-world intended target systems of the modelling, how are we going to be able to evaluate or test the modes? Without giving that kind of information it is impossible to check if the ‘possible world’ models mainstream economists come up with actually hold also for the one world in which we live — the real world.

Om vådan av att läsa nationalekonomi

5 Jul, 2019 at 09:33 | Posted in Economics | 2 Comments

fgbbio_2För egen del … måste jag läsa lika nödvändigt som jag måste andas, och jag kan numera läsa så gott som allt som är någorlunda mänskligt skrivet och har innehåll …

Men bestämda undantag finnas, som jag aldrig kommer att kunna bemästra; liksom vissa människor med god aptit bli sjuka vid tanken på ål eller må illa av musslor. Jag kan inte läsa juridik; inte heller nationalekonomi …

Försöker jag mig på den sortens ting, inträda oförminskade de nybörjarfenomen som kunna hemsöka ett stackars ovilligt skolbarn: universum sjunker samman i grå lump, och själen fylles av kval; förståndet vrider sig, som om det sutte på ett halster, och hjärnan känns vattnig; och en trötthet, långt mer kompakt än all trötthet alstrad av någon som helst av mig känd form av arbete eller förströelse, har redan efter fem minuter hunnit fylla varje fiber av min varelse som en evighetens flod av bly.

Frans G Bengtsson

The explanation paradox in economics

2 Jul, 2019 at 15:28 | Posted in Economics, Theory of Science & Methodology | 7 Comments

hotHotelling’s model, then, is false in all relevant senses … And yet, it is considered explanatory. Moreover, and perhaps more importantly, it feels explanatory. If we have not thought much about Hotelling’s kind of cases, it seems that we have genuinely learned something. We begin to see Hotelling situations all over the place. Why do electronics shops in London concentrate in Tottenham Court Road and music shops in Denmark Street? Why do art galleries in Paris cluster around Rue de Seine? Why have so many hi-fi-related retailers set up business in Calle Barquillo in Madrid such that it has come to be known as ‘Calle del Sonido’ (Street of Sound)? And why the heck are most political parties practically indistinguishable? But we do not only come to see that, we also intuitively feel that Hotelling’s model must capture something that is right.

We have now reached an impasse of the kind philosophers call a paradox: a set of statements, all of which seem individually acceptable or even unquestionable but which, when taken together, are jointly contradictory. These are the statements:

(1) Economic models are false.
(2) Economic models are nevertheless explanatory.
(3) Only true accounts can explain.

When facing a paradox, one may respond by either giving up one or more of the jointly contradictory statements or else challenge our logic. I have not found anyone writing on economic models who has explicitly challenged logic (though their writings sometimes suggest otherwise).

Julian Reiss

The logic of economic models

1 Jul, 2019 at 17:28 | Posted in Economics, Theory of Science & Methodology | 2 Comments

nancyAnalogue-economy models may picture Galilean thought experiments or they may describe credible worlds. In either case we have a problem in taking lessons from the model to the world. The problem is the venerable one of unrealistic assumptions, exacerbated in economics by the fact that the paucity of economic principles with serious empirical content makes it difficult to do without detailed structural assumptions. But the worry is not just that the assumptions are unrealistic; rather, they are unrealistic in just the wrong way.

Nancy Cartwright

One of the limitations with 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 of our wish to discover (causal) relationships between economic ‘variables.’ Galileo's falling bodies experimentIf 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 experiments were according to Nancy Cartwright (Hunting Causes and Using Them, p. 223)

designed to find out what contribution the motion due to the pull of the earth will make, with the assumption that the contribution is stable across all the different kinds of situations falling bodies will get into … He eliminated (as far as possible) all other causes of motion on the bodies in his experiment so that he could see how they move when only the earth affects them. That is the contribution that the earth’s pull makes to their motion.

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 big problem is to decide or find out exactly for which objects air resistance (and other potentially ‘confounding’ factors) is ‘negligible.’ In the case of heavy balls, air resistance is obviously negligible, but how about feathers or plastic bags?

One possibility is to take the all-encompassing-theory road and find out all about possible disturbing/confounding factors — not only air resistance — influencing the fall and build that into one great model delivering accurate predictions on what happens when the object that falls is not only a heavy ball but feathers and plastic bags. This usually amounts to ultimately state some kind of ceteris paribus interpretation of the ‘law.’

Another road to take would be to concentrate on the negligibility assumption and to specify the domain of applicability to be only heavy compact bodies. The price you have to pay for this is that (1) ‘negligibility’ may be hard to establish in open real-world systems, (2) the generalisation you can make from ‘sample’ to ‘population’ is heavily restricted, and (3) you actually have to use some ‘shoe leather’ and empirically try to find out how large is the ‘reach’ of the ‘law.’

In mainstream economics, one has usually settled for the ‘theoretical’ road (and in case you think the present ‘natural experiments’ hype has changed anything, remember that to mimic real experiments, exceedingly stringent special conditions have to obtain).

In the end, it all boils down to one question — are there any Galilean ‘heavy balls’ to be found in economics, so that we can indisputably establish the existence of economic laws operating in real-world economies?

As far as I can see there some heavy balls out there, but not even one single real economic law.

Economic factors/variables are more like feathers than heavy balls — non-negligible factors (like air resistance and chaotic turbulence) are hard to rule out as having no influence on the object studied.

Galilean experiments are hard to carry out in economics, and the theoretical ‘analogue’ models economists construct and in which they perform their ‘thought-experiments’ build on assumptions that are far away from the kind of idealized conditions under which Galileo performed his experiments. The ‘nomological machines’ that Galileo and other scientists have been able to construct have no real analogues in economics. The stability, autonomy, modularity, and interventional invariance, that we may find between entities in nature, simply are not there in real-world economies. That’s are real-world fact, and contrary to the beliefs of most mainstream economists, they won’t go away simply by applying deductive-axiomatic economic theory with tons of more or less unsubstantiated assumptions.

By this, I do not mean to say that we have to discard all (causal) theories/laws building on modularity, stability, invariance, etc. But we have to acknowledge the fact that outside the systems that possibly fulfil these requirements/assumptions, they are of little substantial value. Running paper and pen experiments on artificial ‘analogue’ model economies is a sure way of ‘establishing’ (causal) economic laws or solving intricate econometric problems of autonomy, identification, invariance and structural stability — in the model world. But they are pure substitutes for the real thing and they don’t have much bearing on what goes on in real-world open social systems. Setting up convenient circumstances for conducting Galilean experiments may tell us a lot about what happens under those kinds of circumstances. But — few, if any, real-world social systems are ‘convenient.’ So most of those systems, theories and models, are irrelevant for letting us know what we really want to know.

To solve, understand, or explain real-world problems you actually have to know something about them — logic, pure mathematics, data simulations or deductive axiomatics don’t take you very far. Most econometrics and economic theories/models are splendid logic machines. But — applying them to the real world is a totally hopeless undertaking! The assumptions one has to make in order to successfully apply these deductive-axiomatic theories/models/machines are devastatingly restrictive and mostly empirically untestable– and hence make their real-world scope ridiculously narrow. To fruitfully analyse real-world phenomena with models and theories you cannot build on patently and known to be ridiculously absurd assumptions. No matter how much you would like the world to entirely consist of heavy balls, the world is not like that. The world also has its fair share of feathers and plastic bags.

The problem articulated by Cartwright (in the quote at the top of this post) is that most of the ‘idealizations’ we find in mainstream economic models are not ‘core’ assumptions, but rather structural ‘auxiliary’ assumptions. Without those supplementary assumptions, the core assumptions deliver next to nothing of interest. So to come up with interesting conclusions you have to rely heavily on those other — ‘structural’ — assumptions.

Let me just take one example to show that as a result of this the Galilean virtue is totally lost — there is no way the results achieved within the model can be exported to other circumstances.

When Pissarides — in his ‘Loss of Skill during Unemployment and the Persistence of Unemployment Shocks’ QJE (1992) —try to explain involuntary unemployment, he do so by constructing a model using assumptions such as e. g. ”two overlapping generations of fixed size”, ”wages determined by Nash bargaining”, ”actors maximizing expected utility”,”endogenous job openings”, and ”job matching describable by a probability distribution.” The core assumption of expected utility maximizing agents doesn’t take the models anywhere, so to get some results Pissarides have to load his model with all these constraining auxiliary assumptions. Without those assumptions, the model would deliver nothing. The auxiliary assumptions matter crucially. So, what’s the problem? There is no way the results we get in that model would happen in reality! Not even extreme idealizations in the form of invoking non-existent entities such as ‘actors maximizing expected utility’ delivers. The model is not a Galilean thought-experiment. Given the set of constraining assumptions, this happens. But change only one of these assumptions and something completely different may happen.

Whenever model-based causal claims are made, experimentalists quickly find that these claims do not hold under disturbances that were not written into the model. Our own stock example is from auction design – models say that open auctions are supposed to foster better information exchange leading to more efficient allocation. Do they do that in general? Or at least under any real world conditions that we actually know about? Maybe. But we know that introducing the smallest unmodelled detail into the setup, for instance complementarities between different items for sale, unleashes a cascade of interactive effects. Careful mechanism designers do not trust models in the way they would trust genuine Galilean thought experiments. Nor should they.

A. Alexandrova & R. Northcott

The lack of ‘robustness’ with respect to variation of the model assumptions underscores that this is not the kind of knowledge we are looking for. We want to know what happens to unemployment in general in the real world, not what might possibly happen in a model given a constraining set of known to be false assumptions. This should come as no surprise. How that model with all its more or less outlandishly looking assumptions ever should be able to connect with the real world is, to say the least, somewhat unclear. The total absence of strong empirical evidence and the lack of similarity between the heavily constrained model and the real world makes it even more difficult to see how there could ever be any inductive bridging between them. As Cartwright has it, the assumptions are not only unrealistic, they are unrealistic “in just the wrong way.”

In physics, we have theories and centuries of experience and experiments that show how gravity makes bodies move. In economics, we know there is nothing equivalent. So instead mainstream economists necessarily have to load their theories and models with sets of auxiliary structural assumptions to get any results at all int their models.

So why do mainstream economists keep on pursuing this modelling project?

Continue Reading The logic of economic models…

Vad min kropp behöver

1 Jul, 2019 at 12:00 | Posted in Varia | 3 Comments


Self-righteous Chicago drivel

30 Jun, 2019 at 13:49 | Posted in Economics | 5 Comments

In 2007 Chicago überpriest Thomas Sargent gave a graduation speech at University of California at Berkeley, giving the grads “a short list of valuable lessons that our beautiful subject teaches”:

1. Many things that are desirable are not feasible.
2. Individuals and communities face trade-offs.
3. Other people have more information about their abilities, their efforts, and their preferences than you do.
4. Everyone responds to incentives, including people you want to help. That is why social safety nets don’t always end up working as intended.
5. There are trade offs between equality and efficiency.
6. In an equilibrium of a game or an economy, people are satisfied with their choices. That is why it is difficult for well meaning outsiders to change things for better or worse.
Lebowski.jpg-610x07. In the future, you too will respond to incentives. That is why there are some promises that you’d like to make but can’t. No one will believe those promises because they know that later it will not be in your interest to deliver. The lesson here is this: before you make a promise, think about whether you will want to keep it if and when your circumstances change. This is how you earn a reputation.
8. Governments and voters respond to incentives too. That is why governments sometimes default on loans and other promises that they have made.
9. It is feasible for one generation to shift costs to subsequent ones. That is what national government debts and the U.S. social security system do (but not the social security system of Singapore).
10. When a government spends, its citizens eventually pay, either today or tomorrow, either through explicit taxes or implicit ones like inflation.
11. Most people want other people to pay for public goods and government transfers (especially transfers to themselves).
12. Because market prices aggregate traders’ information, it is difficult to forecast stock prices and interest rates and exchange rates.

Reading through this list of “valuable lessons” things suddenly fall in place. If you ever had doubt about economics as a n ideology, reading Sargent’s list sure gives a clear answer.

What is perhaps even worse is that this kind of self-righteous neoliberal drivel has again and again been praised and prized. And not only by econ bloggers and right-wing think-tanks.

Out of the 79 persons that have been awarded “The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel,” more than 30 have been affiliated to The University of Chicago. The world is really a small place when it comes to economics …

Trösklar och statistisk signifikans

30 Jun, 2019 at 09:48 | Posted in Statistics & Econometrics | 1 Comment

I en artikelEkonomistas argumenterar nationalekonomen Robert Östling för att lösningen på den uppmärksammade ‘replikationskrisen’ är att ändra på tröskeln för vad som ska betraktas som ‘statistiskt signifikant’ från 5% till 0,5%.

Även om detta i sig är vällovligt är det dock ingen lösning. Det räcker inte med att ändra godtyckliga nivåer för vad som ska anses vara ‘statistiskt signifikant’ eller ej. Det är inte där det grundläggande problemet ligger:

worship-p-300x214We recommend dropping the NHST [null hypothesis significance testing] paradigm — and the p-value thresholds associated with it — as the default statistical paradigm for research, publication, and discovery in the biomedical and social sciences. Specifically, rather than allowing statistical signicance as determined by p < 0.05 (or some other statistical threshold) to serve as a lexicographic decision rule in scientic publication and statistical decision making more broadly as per the status quo, we propose that the p-value be demoted from its threshold screening role and instead, treated continuously, be considered along with the neglected factors [such factors as prior and related evidence, plausibility of mechanism, study design and data quality, real world costs and benefits, novelty of finding, and other factors that vary by research domain] as just one among many pieces of evidence.

We make this recommendation for three broad reasons. First, in the biomedical and social sciences, the sharp point null hypothesis of zero effect and zero systematic error used in the overwhelming majority of applications is generally not of interest because it is generally implausible. Second, the standard use of NHST — to take the rejection of this straw man sharp point null hypothesis as positive or even definitive evidence in favor of some preferredalternative hypothesis — is a logical fallacy that routinely results in erroneous scientic reasoning even by experienced scientists and statisticians. Third, p-value and other statistical thresholds encourage researchers to study and report single comparisons rather than focusing on the totality of their data and results.

Andrew Gelman et al.

Vi får aldrig glömma att de underliggande parametrar vi använder när vi gör våra signifikanstestningar är modellkonstruktioner. Oberoende av vlka p-värden vi än får så säger de oss ingenting om modellen är fel. Och framför allt — oberoende av hur många signifikanstester och vilka tösklar vi sätter så validerar de aldrig modeller!

frIn journal articles a typical regression equation will have an intercept and several explanatory variables. The regression output will usually include an F-test, with p – 1 degrees of freedom in the numerator and n – p in the denominator. The null hypothesis will not be stated. The missing null hypothesis is that all the coefficients vanish, except the intercept.

If F is significant, that is often thought to validate the model. Mistake. The F-test takes the model as given. Significance only means this: if the model is right and the coefficients are 0, it is very unlikely to get such a big F-statistic. Logically, there are three possibilities on the table:
i) An unlikely event occurred.
ii) Or the model is right and some of the coefficients differ from 0.
iii) Or the model is wrong.

Chicago style response to critique

29 Jun, 2019 at 18:44 | Posted in Economics | 5 Comments


In a post up here earlier this week yours truly questioned the scientific value of Chicago economics. I took as an example the SMD theorem, that has unequivocally showed that there does not exist any condition by which assumptions on individuals would guarantee neither stability nor uniqueness of a general equilibrium solution — and that it, therefore, is intellectually dishonest to just go on pretending that it is still acceptable to model real-world economies building on the assumption that an entire economy can be modelled as a representative actor and that this is a valid procedure.

And as usual, when those Chicago economists respond to the critique, they
immediately want to divert the attention into focusing on mathematical technicalities.

As if that was the problem! It is not.

The basic problem is that Chicago style models crucially build on unrealistic assumptions known to be false. And since genuine explanations require truth, those models capture nothing of significance and so end up being explanatorily totally irrelevant since they fundamentally misrepresent acting causal factors known to be relevant — they simply lack the necessary representational relationship with the real world.

But reacting the way they do those Chicago economists, should come as no surprise to us since this is the typical Chicago procedure when facing critique. How should one react? Robert Solow knows:

4703325Suppose someone sits down where you are sitting right now and announces to me that he is Napoleon Bonaparte. The last thing I want to do with him is to get involved in a technical discussion of cavalry tactics at the battle of Austerlitz. If I do that, I’m getting tacitly drawn into the game that he is Napoleon. Now, Bob Lucas and Tom Sargent like nothing better than to get drawn into technical discussions, because then you have tacitly gone along with their fundamental assumptions; your attention is attracted away from the basic weakness of the whole story. Since I find that fundamental framework ludicrous, I respond by treating it as ludicrous – that is, by laughing at it – so as not to fall into the trap of taking it seriously and passing on to matters of technique.

Robert Solow

Georgescu-Roegen — the bioeconomic approach to climate change and growth

28 Jun, 2019 at 18:41 | Posted in Economics | 1 Comment

grPositivism does not seem to realize at all that the concept of verifiability — or that the position that ‘the meaning of a proposition is the method of its verification’ — is covered by a dialectical penumbra in spite of the apparent rigor of the sentences used in the argument …

I hope the reader will not take offense at the unavoidable conclusion that most of the time all of us talk some nonsense, that is, express our thoughts in dialectical terms with no clear-cut meaning …

The position that dialectical concepts should be barred from science because they would infest it with muddled thinking, is, therefore, a flight of fancy — unfortunately, not an innocuous one. For it has bred another kind of muddle that now plagues large sectors of social sciences: arithmomania. To cite a few cases from economics alone. The complex notion of economic developmet has been reduced to a number, the income per capita. The dialectical spectrum of human wants … has long since been covered under the colorless numerical concept of ‘utility’ for which, moreover, nobody has yet been able to provide an actual procedure of measurement.

In the postwar period, it has become increasingly clear that economic growth has not only brought greater prosperity. The other side of growth, in the form of pollution, contamination, wastage of resources, and climate change, has emerged as perhaps the greatest challenge of our time.

Against the mainstream theory’s view on the economy as a balanced and harmonious system, where growth and the environment go hand in hand, ecological economists object that it can rather be characterized as an unstable system that at an accelerating pace consumes energy and matter, and thereby pose a threat against the very basis for its survival.

nicholasThe Romanian-American economist Nicholas Georgescu-Roegen (1906-1994) argued in his epochal The Entropy Law and the Economic Process (1971) that the economy was actually a giant thermodynamic system in which entropy increases inexorably and our material basis disappears. If we choose to continue to produce with the techniques we have developed, then our society and earth will disappear faster than if we introduce small-scale production, resource-saving technologies and limited consumption.

Following Georgescu-Roegen, ecological economists have argued that industrial society inevitably leads to increased environmental pollution, energy crisis and an unsustainable growth.

After a radio debate with one of the members of the Nobel prize committee, yours truly asked why Georgescu-Roegen hadn’t got the prize. The answer was — mirabile dictu — that he “never founded a school.” Talk about nonsense! I was surprised, to say the least, and wondered if he possibly had heard of the environmental movement. Well, he had — but it was “the wrong kind of school.” Can it be stated much clearer than this what it’s all about? If you haven’t worked within the mainstream paradigm — then you are excluded a priori from being eligible for the The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel!

Today we really need to re-consider how we look upon how our economy influences the environment and climate change. And we need to do it fast. Nicholas Georgescu-Roegen gives us a good starting point for doing so!

[Added: In case your French isn’t too rusty, here’s a good presentation of his thoughts from France Culture.]

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