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Reasoning in economics

from Lars Syll

Reasoning: Amazon.co.uk: Scriven, Michael: 9780070558823: BooksReasoning is the process whereby we get from old truths to new truths, from the known to the unknown, from the accepted to the debatable … If the reasoning starts on firm ground, and if it is itself sound, then it will lead to a conclusion which we must accept, though previously, perhaps, we had not thought we should. And those are the conditions that a good argument must meet; true premises and a good inference. If either of those conditions is not met, you can’t say whether you’ve got a true conclusion or not.

Mainstream economic theory today is in the story-telling business whereby economic theorists create make-believe analogue models of the target system – usually conceived as the real economic system. This modeling activity is considered useful and essential. Since fully-fledged experiments on a societal scale as a rule are prohibitively expensive, ethically indefensible or unmanageable, economic theorists have to substitute experimenting with something else. To understand and explain relations between different entities in the real economy the predominant strategy is to build models and make things happen in these ‘analogue-economy models’ rather than engineering things happening in real economies.

Mainstream economics has since long given up on the real world and contents itself with proving things about thought up worlds. Empirical evidence only plays a minor role in economic theory, where models largely function as a substitute for empirical evidence. The one-sided, almost religious, insistence on axiomatic-deductivist modeling as the only scientific activity worthy of pursuing in economics, is a scientific cul-de-sac. To have valid evidence is not enough. What economics needs is sound evidence — evidence based on arguments that are valid in form and with premises that are true.

Avoiding logical inconsistencies is crucial in all science. But it is not enough. Just as important is avoiding factual inconsistencies. And without showing — or at least present with a warranted argument — that the assumptions and premises of their models are in fact true, mainstream economists aren’t really reasoning, but only playing games. Formalistic deductive ‘Glasperlenspiel’ can be very impressive and seductive. But in the realm of science it ought to be considered of little or no value to simply make claims about the model and lose sight of reality.

  1. April 9, 2021 at 1:12 pm

    Mainstream economic theory today is in the story-telling business. (Lars Syll)

    Yes, it is true. But the more important question is whether heterodox economic theories have realized something better than the story-telling. If Lars Syll wants to criticize mainstream (he is right in this attempt), he must present comparison between a couple of mainstream and heterodox theories in any theme. If this comparison is impossible, because he cannot find any example in which heterodox theory is really superior than mainstream one, he must present at least a hint for the future of heterodox economics giving an orientation that may produce a better theory than conventional mainstream theories.

    • April 9, 2021 at 2:37 pm

      “Many economists respond to criticism by saying that ‘all models are wrong’ … But the observation that ‘all models are wrong’ requires qualification by the second part of George Box’s famous aphorism — ‘but some are useful’ … The relevant criticism of models in macroeconomics and finance is not that they are ‘wrong’ but that they have not proved useful in macroeconomics and have proved misleading in finance.

      When we provide such a critique, we often hear another mantra to which many economists subscribe: ‘It takes a model to beat a model.’ On the contrary, we believe that it takes facts and observations to beat a model … If a model fails to answer the problem to which it is addressed, it should be put back in the toolbox … It is not necessary to have an alternative tool available to know that the plumber who arrives armed only with a screwdriver is not the tradesman we need.” (John Kay & Mervyn King, “Radical Uncertainty”)

      In comments here on this blog — most often by the duo Shiozawa and Holtham — yours truly is told that as long as he cannot come up with some own alternative model to the failing mainstream models, he shouldn’t expect people to pay attention.

      This is, however, not only wrong for the reasons given by Kay and King, but is also to utterly misunderstand the role of philosophy and methodology of economics. We have to stick to some kind of division of labour also in science. Everyone can’t do everything Clearing obstacles to science by clarifying limits and consequences of choosing specific modelling strategies, assumptions, and ontologies — that’s what philosophy and methodology can contribute to economics. And that’s exactly what yours truly — humbly — tries to do to the best of his ability as an economics philosopher and methodologist.

  2. April 9, 2021 at 3:18 pm

    As a mathematician who struggles to make sense of most philosophy, social science and economics, it seems to me that I am not alone in my lack of comprehension.

    Just taking Lars’ quote of Scriven: really? Can anyone make sense of this?

    On the other hand, I find Kay and KIng much easier to make sense of. For example, their opening quote of Hayek would seem hard to reconcile with Scriven. The book also cites Ken Binmore approvingly. Maybe this is a ‘hint’ for Yoshinori?

    Interestingly, neither author seems to have discussed this with Ken, and Ken has yet to comment on the book (at least to me). (And in any case, I doubt that Lars reads Ken the way I do.)

    I have some notes on the book at https://wordpress.com/page/djmarsay.wordpress.com/5464 which is currently protected by the password ‘King’. This is because I feared that someone like Lars might not read it the way I do, and possibly his views are closer to theirs than mine.

    Looking at GoodReads just now, it seems the book hasn’t been taken in the way I’d hoped. Maybe we need a credible account of ‘reason’ to counter Scriven? Ideas, anyone?

  3. Gerald Holtham
    April 9, 2021 at 7:43 pm

    To be clear, I don’t expect Lars Syll to come up with alternative economic theory. If I haven’t done it myself, how can I demand that he should? And I share his frustration with the status quo. What worries me is his hostile attitude to the only techniques we have for imposing empirical discipline on economics. Not hostile to misuse of these techniques, which is common and deserves to be nailed, but to the techniques themselves. When someone (rightly) decries the absence of empirical content and realism in economic models and then attacks, in principle, the methods we have to test theory against fact, well I think we are entitled to be a bit bewildered. And we are entitled to ask him how he thinks we can move forward.

    • April 10, 2021 at 4:31 pm

      I’m struggling here. But if you look at Lars’ response you will see that he lists a long list of incredible assumptions. My (limited) experience is that while these assumptions are almost always false, you can nevertheless often find ways of normalising problems to make them true, and this can provide insight.

      For example, the notion that people maximize utility is obviously wrong but sometimes ‘fruitful’. In particular, it seems important to point out that in so far as utility is a relevant concept, wealth isn’t just money and there seems to be such a thing a ‘social capital’ etc.

      But my deeper concern is that whatever mathematicians say on any subject other than mathematics, they get misunderstood, sometimes with serious impact. For example, I’m currently trying to make sense of the UK approach to Covid, which has got me very confused and concerned about the social sciences notion of ‘expert’. As with Scriven, logic doesn’t seem to be a relevant requirement. So maybe the ‘deeper’ problem is that economists need to understand logic before they can make genuine progress? (Or possibly some social scientist somewhere could finesse the issue and develop some adequate notions of ‘reason’ and ‘rationality’?)

  4. pfeffertag
    April 9, 2021 at 9:31 pm

    “The one-sided, almost religious, insistence on axiomatic-deductivist modeling as the only scientific activity worthy of pursuing in economics, is a scientific cul-de-sac.”

    I suggest the cause of the cul-de-sac is not the deductivism but the limited axioms. Specifically the cause is the omission of society from the axioms, the limitation to individuals trading with each other.

    “What economics needs is sound evidence … with premises that are true.”

    I don’t see the usefulness of evidence here. Surely the most excellent evidence could only confirm the modelling is faulty.

    As for “premises that are true”—that is not how science theory works. On the contrary, the premises—the hypotheses or axioms—express a relationship which is fictional, and which is extreme.

    Here are some examples: Newton’s gravity theory interrelates two bodies which are isolated, perfectly spherical, and of uniform density—conditions which nowhere exist. Aerodynamic theory hypothesises imaginary layers of incompressible, friction-free air flowing over smooth surfaces, not fluttering leaves. Galileo theorised gravity as a perfect sphere rolling on a perfect plane which it touches at a single point, not landslides. Gas theory refers to an “ideal gas” in an enclosed tank, not in volcanoes. A theoretical pendulum is friction-free with a string of no weight and a weight of no size. These unrealistic theories apply to, and are necessary to understand, stars, leaves, landslides, volcanoes and real pendulums.

    Science theory states an extreme relationship between extreme, or idealised, concepts. Those examples are just as unrealistic as economic theory. Used alone they would also lead to faulty answers and the solution is to introduce additional theories to account for other factors. The analogous solution to the inadequacies of economics is to introduce additional theories. That is, don’t abandon present theory; broaden the deductive theory to new axioms which describe society, in addition to individuals.

  5. Gerald Holtham
    April 9, 2021 at 10:45 pm

    But science doesn’t take theories on trust because they are beautiful, elegant or just sanctified by familiarity. It insists on tests – by controlled experiment if possible, by a mixture of sample studies and statistical testing otherwise. Theories that fail the tests are eventually discarded or, at least, parked and not employed. Economics hides behind the complexity and mutability of reality and the fact that no test is 100 per cent conclusive in order to ignore empirical results that are inconvenient.
    In a world of mortals with imperfect knowledge, how informative is theorising that supposes people who live forever or who live in a world of two generations and who know everything up to a white noise error process? You might suppose it would not be very informative but you can be sure only when you test. When you test and find results incompatible with the theory that should be the end of the matter. Let’s try something else. Not in economics. There is someone in a university somewhere right now seeking a publication by playing tunes on a two-generation rational expectations model of something or other, ignoring aggregation issues.
    I don’t deny many of the theoretical constructs of economics are aids to thought in analysing real situations. Most of the useful constructs are quite old and long in the tooth, however. The accretion of more recent theory has led to severely diminishing returns in terms of practical understanding of the world. There are ideological and sociological reasons no doubt but it is possible because economics won’t let beautiful theories be slain by ugly facts.

    • April 10, 2021 at 4:09 pm

      Gerald, at the risk of seeming pedantic and possibly distractive … .

      Science as a product is inevitably shaped by something like Ockham’s razor and selected to be accessible, and hence inevitably reflects somewhat arbitrary social constructs and tastes. I agree with you that ‘actual’ scientists don’t trust such simplifications, and pay much more heed to actual empirical results. But many (even some with science ‘training’) tend to ‘follow the science’, or to encourage others to do so, without distinguishing between that part of the product that is actually scientific and that which reflects current prejudices.

      In ‘hard’ sciences this doesn’t seem to matter too much, because they are interested in ‘testable’ properties. Economics is a different matter, though.

  6. April 10, 2021 at 8:34 am

    Gerald Holtham maintains that “science doesn’t take theories on trust because they are beautiful, elegant or just sanctified by familiarity. It insists on tests …” Testing is, of course, a significant scientific activity. But how far doe the testing instruments we have at our disposal really take us?

    The favourite instrument among economists nowadays is some kind of econometric procedure. Econometrics is supposed to be able to test economic theories, but to serve as a testing device you have to make many assumptions, many of which themselves cannot be tested or verified. To make things worse, there are also only rarely strong and reliable ways of telling us which set of assumptions is to be preferred. Trying to test and infer causality from (non-experimental) data you have to rely on assumptions such as disturbance terms being ‘independent and identically distributed’; functions being additive, linear, and with constant coefficients; parameters being’ ‘invariant under intervention; variables being ‘exogenous’, ‘identifiable’, ‘structural and so on. Unfortunately, we are seldom or never informed of where that kind of ‘knowledge’ comes from, beyond referring to the economic theory that one is supposed to test. Performing technical tests is, of course, needed, but perhaps even more important is to be aware that we regularly face situations where the testing assumptions of the tests do not fit the data.

    That leaves us in the awkward position of having to admit that if the assumptions made do not hold, the inferences, conclusions, and testing outcomes econometricians come up with simply do not follow from the data and statistics they use.

    The central question is: how do we learn from empirical data? Testing statistical/econometric models is one way, but we have to remember that the value of testing hinges on our ability to validate the (often unarticulated technical) assumptions on which the testing models build. If the model is wrong, the test apparatus simply gives us fictional values. There is always a strong risk that one puts a blind eye on some of those non-fulfilled technical assumptions that actually makes the testing results — and the inferences we build on them — unwarranted.

    • April 10, 2021 at 4:19 pm

      Lars, how could Scriven’s notion of rationality be given any credibility? I can only imagine it hasn’t been tested very well. I think you are on to something when you say that economic theories haven’t been tested very well

      But may I quibble with:
      “Trying to test and infer causality from (non-experimental) data you have to rely on assumptions such as disturbance terms being ‘independent and identically distributed’; functions being additive, linear, and with constant coefficients; parameters being’ ‘invariant under intervention; variables being ‘exogenous’, ‘identifiable’, ‘structural and so on.”

      I’m not sure what you mean by ‘causality’ in the social sciences, but it seems to me that in thinking about complex subjects it is mistake to confine rely on such incredible assumptions. In this, I agree with Keynes et al.

  7. Gerald Holtham
    April 10, 2021 at 7:34 pm

    Lars, it is not only econometrics that is at issue. You have also been dismissive of the attempt to use empirical testing or pilot studies to evaluate different economic approaches. Your attack is based on the possibility that confounding variables will have been missed or not controlled for and results may depend on particularities. Of course those are always possibilities. We know that philosophical scepticism cannot be refuted; if you insist on perfection in knowledge you never have to believe anything. But knowledge usually advances a bit at a time. A well-conducted experiment will be informative if rarely completely conclusive about all the open questions. Does that mean it is better not to do it? I do find it strange that you should have taken such an aversion to the work of DuFlo et al
    Now when it comes to econometrics you rely on criticisms that are somewhat out of date. I do not claim that econometric testing is more likely to be conclusive than pilots and experiments. But carefully done it can similarly advance knowledge. The debate about what is needed to make econometric tests legitimate and useful goes back to Hendry’s seminal article: Hendry, D. F. (1980). Econometrics: Alchemy or science? Economica 47, 387–406.
    In which he describes how econometrics is commonly misused and sets out the route to move from alchemy to something more scientific. Chris Gilbert had further exposition of the approach in Oxford Economic Papers in 1986. Things have moved on much further since then with new tests for spurious correlation and for when breaks in series indicate that relationships have shifted or broken down.
    We are in danger of going around in circles because you keep repeating : “you have to rely on assumptions such as disturbance terms being ‘independent and identically distributed’; functions being additive, linear, and with constant coefficients; parameters being’ ‘invariant under intervention; variables being ‘exogenous’, ‘identifiable’, ‘structural and so on” and I keep repeating that those assumptions can all be tested. If you find that observable error terms are not independent and identically distributed that in itself is evidence of specification error. People can ignore tests in order to push a particular theory but that doesn’t mean the tests don’t exist. And one is not obliged to assume linearity or constant coefficients, for example. The limitation on what you can do is imposed by data availability. You can easily test for functional form and the linearity assumption but the complexity of the specification is of course limited by the amount of data one has.
    I have real difficulty in understanding why we should disagree about this. I freely acknowledge that given data limitations econometric tests will frequently have low power to discriminate among hypotheses. But some nonsense can be knocked out, some propositions can be shown to be incompatible with some historical experience. I don’t claim we have the philosopher’s stone but you should be able to admit we have a potentially useful instrument that in the right hands enables us to make progress. The problem at present is that there is not enough empirical testing of economic models and some continue to be used for forecasting or policy prescription that are incompatible with the data. We should not need an event like 2008 to tell us that models are wrong when the data have been shrieking it for years.
    Now, causality. Correlation is not causality – every one acknowledges that. When someone proposes any economic model or theory they are asserting a causal story. All we can do is ask whether that story is compatible with the facts, with the data we have. Even if it is compatible, the data do not prove the story is correct; future events may prove it wrong or confined to special cases. But if the data could not have been generated by the model as specified then we have learned something and shouldn’t use the model without further work and amendment. Econometrics cannot infer causation – suck causal stories out of data; it can only say (sometimes) whether or not a given causal story is tenable as specified.
    Finally : invariance of parameters given an intervention. Discussing this will make a long post even longer. Robert Lucas took economics on a wild-goose chase trying to specify models that one knows will be invariant to policy intervention. It is obvious he failed. In practice if something happens that has not happened before you cannot be sure that parameters of a model won’t change. That is an issue if you want to use a model for policy simulation but how we deal with it I must leave for another time.

    • April 11, 2021 at 10:18 am

      Gerry, I think it is OK that we may have different views on the testing ability of statistics and econometrics (although I definitely think you are plain wrong in maintaining that EVERY econometric assumption is testable — not even our econometrics textbook writers hold to that panglossian view).

      When debating econometrics and its short-comings I often get the response from its practitioners that “ok, maybe econometrics isn’t perfect, but you have to admit that it is a great technique for empirical testing of economic hypotheses.” I usually respond by referring to the text below …

      “Most econometricians today … believe that the main objective of applied econometrics is the confrontation of economic theories with observable phenomena. This involves theory testing, for example testing monetarism or rational consumer behaviour. The econometrician’s task would be to find out whether a particular economic theory is true or not, using economic data and statistical tools. Nobody would say that this is easy. But is it possible? This question is discussed in Keuzenkamp and Magnus (1995). At the end of our paper we invited the readers to name a published paper that contains a test which, in their opinion, significantly changed the way economists think about some economic proposition. Such a paper, if it existed, would be an example of a successful theory test. The most convincing contribution, we promised, would be awarded with a one week visit to CentER for Economic Research, all expenses paid. What happened? One Dutch colleague called me up and asked whether he could participate without having to accept the prize. I replied that he could, but he did not participate. Nobody else responded. Such is the state of current econometrics.”

      (Jan Magnus, “The success of econometrics”, De Economist, 1/1999)

  8. April 11, 2021 at 2:36 pm

    When we provide such a critique, we often hear another mantra to which many economists subscribe: ‘It takes a model to beat a model.’ On the contrary, we believe that it takes facts and observations to beat a model …
    (Lars Syll on April 9, 2021 at 2:37 pm)

    “‘It takes a model to beat a model'” is just the mantra that Lars Syll repeats. He is always confusing (most probably intentionally) models and theories. Does it mean that he has no knowledge of theories? Isn’t it the poverty of a philosopher of economics philosophy and methodology? Without no theory background mere facts do not make sense. Even a very rude confirmation of a fact presumes a rude theory.

    To confirm an event or series of events as a fact, statistic is often necessary (if it is not econometrics). Doesn’t Lars Syll know that the great Gauss started to use the least square method in order to find the most probable orbit of celestial bodies? If the actual econometrics that aims to test a macroeconomic phenomenon is wrong, we needs statistics and a background theory (or better a system of hypotheses) to determine whether a hypothesis is good or to be rejected.

    What worries me is his hostile attitude to the only techniques we have for imposing empirical discipline on economics. Not hostile to misuse of these techniques, which is common and deserves to be nailed, but to the techniques themselves. When someone (rightly) decries the absence of empirical content and realism in economic models and then attacks, in principle, the methods we have to test theory against fact, well I think we are entitled to be a bit bewildered. And we are entitled to ask him how he thinks we can move forward.
    (Gerald Holtham on April 9, 2021 at 7:43 pm)

    I support Gerald Holtham. Holtham and I are asking how we can move forward. Economics evolves through trials and errors. At times we must retreat with a great leap backward. But, this must be done in view of moving forward. Categorical refutation of this and that method only contribute to break down economics and nothing gives birth.

  9. April 11, 2021 at 2:46 pm

    Sorry! I missed out a closing slash for the blockquote tag. The following, I hope, is the corrected one:

    When we provide such a critique, we often hear another mantra to which many economists subscribe: ‘It takes a model to beat a model.’ On the contrary, we believe that it takes facts and observations to beat a model …
    (Lars Syll on April 9, 2021 at 2:37 pm)

    “‘It takes a model to beat a model’” is just the mantra that Lars Syll repeats. He is always confusing (most probably intentionally) models and theories. Does it mean that he has no knowledge of theories? Isn’t it the poverty of a philosopher of economics philosophy and methodology! Without no theory background mere facts do not make sense. Even a very rude confirmation of a fact presumes a rude theory.

    To confirm an event or series of events as a fact, statistic is often necessary (if it is not econometrics). Doesn’t Lars Syll know that the great Gauss started to use least square method in order to find the most probable orbit of celestial bodies? If the actual econometrics that aims to test a macroeconomic phenomenon is wrong, we needs statistics and a background theory (or better a system of hypotheses) to determine whether a hypothesis is good or to be rejected.

    What worries me is his hostile attitude to the only techniques we have for imposing empirical discipline on economics. Not hostile to misuse of these techniques, which is common and deserves to be nailed, but to the techniques themselves. When someone (rightly) decries the absence of empirical content and realism in economic models and then attacks, in principle, the methods we have to test theory against fact, well I think we are entitled to be a bit bewildered. And we are entitled to ask him how he thinks we can move forward.
    (Gerald Holtham on April 9, 2021 at 7:43 pm)

    I support Gerald Holtham. Holtham and I are asking how we can move forward. Economics theory evolves through trials and errors. In sometime we must retreat with a great leap backward. But, this must be in view of move forward. Categorical refutation of this and that method only contribute to break down economics and nothing gives birth.

  10. Gerald Holtham
    April 11, 2021 at 2:58 pm

    Well, I think Hendry knocked out monetarism by showing that the velocity of circulation of money was not stable or mean reverting in the US but had a unit root, i.e. it wandered about. Money has also been found not to be a significant leading indicator for nominal GDP in most countries when other variables are included in the equation. It is not found in the OECD’s composite leading indicator for GDP in most countries. for example. No country now targets monetary aggregates and few economists would assert there is a stable “demand for money”. I think we did scotch that one. As for rational expectations, I know of no examination that has found it data compatible. When it is forced into models on theoretical or ideological grounds forecasting properties, seldom great to start with, always deteriorate. See, for example, “The future of macroeconomics: macro theory and models at the Bank of England” David F Hendry, John N J Muellbauer, Oxford Review of Economic Policy, Volume 34, Issue 1-2, Spring-Summer 2018, Pages 287–328.
    The problem is finding economic theories that do pass tests not finding ones that don’t.
    But there is, of course, a difficulty. Economic theories are usually specified with some supporting assumptions that make them usable and implementable in practice. When they are found to be at variance with data it is always possible to blame the supporting assumptions since one is, in fact testing joint hypotheses. This is a more general problem than one of econometrics. Philosophers of science have observed that refutation can always be avoided in any science if you add conditions to the the law you are proposing. Each attempted refutation just invites another condition. The result is what Lakatos called a “degenerating research strategy”. It has not been subject to a single conclusive refutation but its prestige and promise decline every time a new excuse has to be made. I think that is where we are with contemporary macroeconomics. If the fight judge is unbiased he will award econometrics a technical knock-out. Even if he won’t do that, it should be winning on points. Apart from the logical difficulty of testing joint hypotheses there is also the sociological one that people heavily invested in a theory just ignore contrary evidence.
    As for testing every statistical assumption, you can’t test everything at once but you can sequentially test each of the assumptions that you have mentioned in your posts as essential. I don’t expect our views to converge entirely. Warning how easy it is to misuse econometrics is fine with me too. I just don’t think it is salutary to issue blanket condemnations of the empirical methods we have when a huge problem in our subject is a tendency to esteem theoretical neatness over empirical applicability.

  11. April 11, 2021 at 4:51 pm

    Gerry, there certainly is much that we agree on. But even if you “sequentially test” there still are things you NEVER can decisively test. Let me just take one example from the world of econometric evaluations of RCTs. Nowadays the diff-in-diff evaluation method is very popular. To apply it you, however, have to ASSUME that the diff in the ‘control’ group IS the counterfactual diff you would get if the ‘treatment’ group wasn’t treated. If you perform lots of comparisons between groups you may, of course, feel more or less CONFIDENT in your identification assumption (of pre-treatment equality between groups) but — again — the assumption is simply UNTESTABLE no matter how you sequence or repeat your tests!

    • April 11, 2021 at 5:04 pm

      Lars: please could you recommend a paper on these methods for a mathematician who has so far failed to make much sense of how social scientists apply stats? It would be interesting to see how well they stand up to Keynes’ critique.

      • April 11, 2021 at 5:11 pm

        Dave, if you’re not familiar with them yet, I suggest you have a look at Pischke and Angrist, Mostly harmless econometrics, Princeton University Press, 2009.

      • April 11, 2021 at 5:38 pm

        Thanks, Lars. I couldn’t find any references to anything that I might credibly claim to understand. For example:
        “Familiarity with fundamental probability concepts like mathematical expectation is also helpful, but extraordinary mathematical sophistication is not required.”
        seems reasonable. But how to reconcile this with the following?
        “A causal relationship is useful for making predictions about the consequences of changing circumstances or policies; it tells us what would happen in alternative (or counterfactualî) worlds.”

        The authors seem to assume that their readers will be able to make sense of this. But I side with (my reading of) Keynes on this. Can anyone suggest anything on ‘mathematical expectation’ that might make sense to a mathematician in this context?

        I agree with this. But

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