Interview with Tony Lawson

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Lars Pålsson Syll
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Interview with Tony Lawson

Jamie Morgan: Let’s return to your work on economics and place this in the context of how you came to work in the field. As suggested in the biographical introduction, you have been closely associated with the critique of the ‘formalisation’ of theory and the dominance of mathematical modelling, including the use of statistical techniques, not least econometrics. Were you always critical of these?

Tony Lawson: I was. Or at least I was from the moment I moved across to economics. I first completed a degree in mathematics. Pure maths. I loved the subject. I was set to do a PhD in either Group Theory or Ring Theory at Queen Mary College in Cambridge. But I was aware I would be feeling more and more isolated if I took that road, with few people in the world to discuss my research with. Additionally, I was aware that I was fairly ignorant about how the social world, capitalism, money, etc., worked. Indeed, I knew almost nothing of economics, and yet I was getting quite heavily involved in student politics. So, I decided to go for a change. I took up an MSc in Economics at the LSE instead.

However, I was equally ignorant of the situation in academic economics. I had not the slightest notion that it would be all about mathematical modelling. So, in my very first lecture I asked the lecturer why he kept trying to express everything in terms of mathematical models, focussing only on comparing different ones. I was keen to learn about social reality, and the models seemed to me to be an obvious distraction. The question did not go down well. I felt in my gut that these methods were inappropriate, given the nature of social reality. However, I was not in a position to articulate that view very well. I needed to be able to express my intuitions in a manner that I could easily communicate to others. That is where my excursions into social ontology effectively started; and that is also when I started criticizing the emphasis on methods of mathematical modelling as tools for social analysis. There was never a moment that I thought that seeking mathematically to model human behaviour was, in most situations anyway, other than absurd.

But I was very naïve about the state of academic economics. I was always surprised that criticizing this modelling emphasis was received so badly. There was never much of a reasoned defence of it provided by anyone. Mostly the reaction has been annoyance. Of course, the very reason I was so readily accepted into economics was the same reason that a critique of modelling has had so little impact. Economists, in my experience, were and are in awe of mathematicians. In addition, they seem to think not only that economics must be a science, but that mathematics is essential to science, and ‘so’ they have to do it. It’s a form of methodological ideology, and it is very difficult to shift.

Journal of Critical Realism

To have ‘consistent’ models and ‘valid’ evidence is, as Lawson again and again has reminded us, not enough. Models may help us think through problems. But we should never forget that the formalism we use in our models is not self-evidently transportable to a largely unknown and uncertain reality. The tragedy with mainstream economic theory is that it thinks that the logic and mathematics used are sufficient for dealing with our real-world problems. They are not. Model deductions based on questionable assumptions can never be anything but exercises in hypothetical reasoning.

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.

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.

‘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 do not know.

Mathematics and logic cannot establish the truth value of facts. Never has. Never will.

[Added: For those who read Swedish, my article on Tony Lawson’s thoughts on economics and ontology published in Fronesis 54-55 may be of interest.]