Paul Romer is ‘busy’ …

19 May, 2015 at 14:21 | Posted in Economics | 9 Comments

About math: I have an undergraduate degree in physics. I’ve seen clear evidence that math can facilitate scientific progress toward the truth.

If you think that math is worthless or dangerous, I’m sure that there are people who will be happy to discuss this with you. I’m not interested. I’m busy.

too-busy-people-workplace-ecard-someecardsAbout truth and science: My fundamental premise is that there is an objective notion of truth and that science can help us make progress toward truth.

If you do not accept this premise, I’m sure that there are people who would be happy to debate it with you. I’m not interested. I’m busy.

Paul Romer

Hmm …

To me this sounds more like a person afraid of methodological self-reflection, rather than an open-minded and pluralist person.

Where does this methodology-aversion come from?

As far as yours truly can see it all grinds down to a misplaced belief in deductivist mathematical reasoning being the only kind of scientific economics around. If economics isn’t performed as a mathematical modeling it’s not really science in Romer’s world-view. There is no problem with that view — as long as you have done some ontological and methodological reflection and presented arguments for the appropriateness of insisting on deductivist-mathematical modeling being the preferred scientific procedure in economics. No such argumentation is presented.

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

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

The world in which we live is inherently uncertain and quantifiable probabilities are the exception rather than the rule. To every statement about it is attached a “weight of argument” that makes it impossible to reduce our beliefs and expectations to a one-dimensional stochastic probability distribution. If “God does not play dice” as Einstein maintained, I would add “nor do people”. The world as we know it, has limited scope for certainty and perfect knowledge. Its intrinsic and almost unlimited complexity and the interrelatedness of its organic parts prevent the possibility of treating it as constituted by “legal atoms” with discretely distinct, separable and stable causal relations. Our knowledge accordingly has to be of a rather fallible kind.

To search for precision and rigour in such a world is self-defeating, at least if precision and rigour are supposed to assure external validity. The only way to defend such an endeavour is to take a blind eye to ontology and restrict oneself to prove things in closed model-worlds. Why we should care about these and not ask questions of relevance is hard to see. We have to at least justify our disregard for the gap between the nature of the real world and our theories and models of it.

Now, if the real world is fuzzy, vague and indeterminate, then why should our models build upon a desire to describe it as precise and predictable? Even if there always has to be a trade-off between theory-internal validity and external validity, we have to ask ourselves if our models are relevant.

Models preferably ought to somehow reflect/express/correspond to reality. I’m not saying that the answers are self-evident, but at least you have to do some methodological and philosophical under-labouring to rest your case. Too often that is wanting in modern economics, where methodological justifications of chosen models and methods as a rule are non-existent.

“Human logic” has to supplant the classical, formal, logic of deductivism if we want to have anything of interest to say of the real world we inhabit. Logic is a marvellous tool in mathematics and axiomatic-deductivist systems, but a poor guide for action in real-world systems, in which concepts and entities are without clear boundaries and continually interact and overlap. In this world I would say we are better served with a methodology that takes into account that “the more we know the more we know we don’t know”.

The models and methods we choose to work with have to be in conjunction with the economy as it is situated and structured. Epistemology has to be founded on ontology. Deductivist closed-system theories, as all the varieties of the Walrasian general equilibrium kind, could perhaps adequately represent an economy showing closed-system characteristics. But since the economy clearly has more in common with an open-system ontology we ought to look out for other theories – theories who are rigorous and precise in the meaning that they can be deployed for enabling us to detect important causal mechanisms, capacities and tendencies pertaining to deep layers of the real world.

Rigour, coherence and consistency have to be defined relative to the entities for which they are supposed to apply. Too often they have been restricted to questions internal to the theory or model. But clearly the nodal point has to concern external questions, such as how our theories and models relate to real-world structures and relations. Applicability rather than internal validity ought to be the arbiter of taste.

But obviosly Paul Romer doesn’t want to talk about these scary methodological-philosophical issues. He is ‘busy’ …

9 Comments »

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  1. Excellent response. Romer’s pomposity reveals volumes.

  2. I sometimes think engaging with these guys is sometimes pointless. I guess that is why people like Maddison didn’t. They just get on with it and don’t worry about being in the mainstream. Does he ever ask fundamental questions? Why is it that the economic problem is posed the way it is? Why do we not have the reverse – limited wants and unlimited resources? What are these budget and other constraints that are in micro-theory and even imposed on complex social systems in macro? Why do we start from such micro-foundations which have no ontological or epistemological basis? Who came up with growth theory, where and why? Does it explain why Britain was the first country to industrialise?Can you explain why the theory, and the economics discipline itself was made up and Britain and a certain type of economics taken up in the United States? Were the reasons practical and political, or scientific? Why is that some countries grow and others don’t? Why are they concentrated in the Europe, North America and East Asia? Why in history have some countries declined in history both absolutely and relatively? Are you going to understand that with your growth theory, which is pretty much historically vacuous (the person who really made an honest attempt to reconcile history and growth theory was Piketty, who by the way will probably the first person to tell you that it is not want is in the theory but not in it that is most interesting – and if you want to find out the answers to these sorts of questions you have to engage with model and maths – free history and other disciplines).

  3. I thought you might be interested in the Society for Economic Science with Heterogeneous Interacting Agents. I checked the proposed algorithm for applying Agent Based Modeling in economics at the website of Professor Leigh TESFATSION, Iowa University, U.S.A. From what I saw, it assumes the ergodic hypothesis. Alas!

    • Reply to “Steve, 19 May 2015”: Ergodicity is by no means a necessary requirement for agent-based modeling. In particular, no mention of ergodicity appears in any of my agent-based computational economics (ACE) work because the hallmark of ACE is the ability to represent economic processes as open-ended dynamic systems of interacting agents with no need to phase-average or time-average, let alone equate the two. See anything at the following ACE resource site to verify this fact:

      http://www2.econ.iastate.edu/tesfatsi/ace.htm

      Sigh…

      Leigh Tesfatsion

  4. Math helps physics – according to his undergraduate findings

    So the same math must help economics

    Seems just a tad silly

    I wonder just how much mathematics he knows – to make such a general claim about such a specific experience

    Math is a big subject too

    Not daring to say physics and economics have their differences

  5. Lars,
    Is there a paper or post you can point to explaining why Romer’s “endogenous growth theory” was and remains such a mountain of a big deal for neoclassical economists?
    I read the paper way back when a grad student at Romer’s institution, it wasn’t useful for what I was doing to warrant my crawling back through its APA-style parenthetical citations, so I never had more than the haziest sense of exactly what problem the thing was meant to have solved.

  6. Also, what most economists label as “a methodology discussion” is identical to what most scientists would call “a scientific discussion.”
    .
    If you “don’t do” methodology, you don’t do science.

  7. Here is why math is needed in economics:

    <>

    Partha Dasgupta, “Modern economics and its critics,” in (ed.) Uskali Mäki, Fact and Fiction in Economics: Models, Realism and Social Construction, CUP: 2002

    • After reading the abstract, I recognize this as but one example in an enormous literature. Surprisingly enormous considering it amounts to one declarative statement:

      You criticize us for doing x, but we don’t do x anymore, we do z, and even when we did x, it was much more nuanced than you understand.

      These rejoinders all suffer from a failure of intellectual imagination. The philosopher is able to abstract from x and see a meta-level methodology while the economist is stuck at the level of x and z.


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