Home > Uncategorized > Krugman’s modeling flim flam

Krugman’s modeling flim flam

from Lars Syll

Paul Krugman had a piece up on his blog last week arguing that the ‘discipline of modeling’ is a sine qua non for tackling politically and emotionally charged economic issues:

You might say that the way to go about research is to approach issues with a pure heart and mind: seek the truth, and derive any policy conclusions afterwards. But that, I suspect, is rarely how things work. After all, the reason you study an issue at all is usually that you care about it, that there’s something you want to achieve or see happen. Motivation is always there; the trick is to do all you can to avoid motivated reasoning that validates what you want to hear.

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

Hmm …  

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

  1. Alejandro Nadal
    May 3, 2016 at 1:58 am

    Great entry and to the point. And I know you also wanted to include in the list of mainstreamers’ assumptions gross substitutability, the weak axiom of revealed preferences at the market level and, of course, transversality conditions. Let’s all thank Paul now. Nice way to tackle politically and emotionally charged issues.

  2. originalsandwichman
    May 3, 2016 at 4:52 am

    The interpersonal utility of these models is incomparable!

  3. May 3, 2016 at 6:46 am

    The futurist and evolutionary economist Hazel Henderson once pointed out that we can study only what we can model. And we study what we care about. So to construct our relationships we model what we want to happen, what we want to find, what we believe is moral, correct, and necessary. And this includes science and technology. When we build our way of life together we try out things, take various paths — we are creative. This is modeling. Formal mathematical and computer modeling is just an extension of this. The formal stuff like this comes along later, after the work of making something like a culture, social movement, government, etc. has been begun by the ordinary “folks on the street.” The formal or scientific work reveals and describes the work of the folk. It is not appropriate for the scientist or logician to substitute the formal models for what the ordinary folk have created. Which, by the way is the major sin of economists. So yes, formal scientific models can take us places we had not intended to go, but when such modeling is applied in the social sciences they should not take us away from what ordinary folk have created. That next step takes us into policy (actions) guidance and recommendations, and away from social science.

  4. Hepion
    May 8, 2016 at 11:59 am

    Curiously, even if Krugman is cheerleader for modeling, he would not accept complex models build on computers, that, say, Steve Keen uses. What he really means is simple models, build on a simple logic.

    But can you model truly complex system, say for example process of evolution, on a simple model? Of course not. There is so much more than just a survival of the fittest.

    What seems to be happening is that they are fooling themselves that these models capture everything, and they apply some ad hoc psychological reasoning to the macro-economy, as if it were sentient being, and then in their delusion they are utterly convinced they have got it right and everyone else is just a fool.

    Competent modeling of the economy would at least include balance-sheets. And be stock-flow consistent.

  5. Hepion
    May 8, 2016 at 12:10 pm

    It’s ‘economic of delusion’ all the way. I have heard that phrase applied to communism but it suits mainstream economics as well. We all want to be polite but unfortunately their ideas are hurting millions of people around the globe.

  6. May 8, 2016 at 7:30 pm

    Models are used by every science. Even a research hypothesis is a simple model. But before a scientist concludes a model is a reasonable stepping off place to more research and further models the scientist must make all efforts to observe the relationship between that model and events supposedly captured within the model. The model is not an end in itself but one of many tools scientists use to find better ways to see what’s happening in the world and to describe it. That’s not to say models can’t lead us in new and unexpected directions. They can and do. But only if they begin that process from at least a reasonably accurate representation of events in the world. Otherwise scientists would quickly spin off into fantasy and lose touch with the things they are supposedly revealing. It is unfortunate that economists by and large have failed to follow these simple requirements for building and using models. Not only leads to nonsense research results but also to policy proposals that make no sense to most people and create/support dysfunctional economies. And on a personal note, as a mathematician I consider economists’ use of (abuse of) mathematics upsetting, dishonest, and disrespectful (of mathematics and the events economists apply it to).

    • May 12, 2016 at 12:46 am

      Here, here! I agree with everything you’ve said {and probably what you’ve left unsaid}. Now that I know that you’re a mathematician :: and I am an idiot when it comes to anything beyond some very basic algebra (and even more basic calculus) :: I might use some to discuss with you about how I bring objective benefits into economics while, simultaneously, showing that even if the mathematics of ‘utility’ were correct (which it cannot be), ‘rational’ behavior would require neither maximizing utility nor the standard ‘fixed’ budgets given consumers. Much of what I have to say can be generalized {mathematically} across sets of goods delivering the same and different benefits, albeit doing so is well beyond my rudimentary mathematical depth.

      • May 12, 2016 at 5:28 am

        Two books to take a look at. “The Mathematical Experience” by Philip Davis and Reuben Hersh. Perfect for social scientists. “Chaos: Making a New Science” by James Gleick. Not an easy book but economists simply cannot continue to ignore Chaos theory and mathematics. I’d also like to recommend a book on quantum theory for economists but don’t know of one. Like Chaos economists cannot continue to ignore quantum theory and relativism. The world’s come a long way since Adam Smith. Unfortunately many economists have not kept up.

      • David Chester
        May 12, 2016 at 8:00 am

        Larry, you don’t need to be cleaver at mathematics to read and understand the relatively simple kinds of algebra used in economics. Please don’t be afraid (as many seem to be) to try. Mathematics is a shorthand way of expressing rather simple and obvious thoughts, and the advantage in using it is that these thoughts can then be manipulated to give results that word-expressed arguments would get too tangled-up to remain clear.

      • May 13, 2016 at 1:17 am

        Thank you, David. I just sent Ken Zimmerman a brief reply to two questions he asked me on another thread. You might want to look at it. I will follow up with a mathematical approach, but, for me, that is more difficult.

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