by Why all models are wrong Models share three common characteristics: First, they simplify, stripping away unnecessary details, abstracting from reality, or creating anew from whole cloth. Second, they formalize, making precise definitions. Models use mathematics, not words … Models create structures within which we can think logically … But the logic comes at a cost, which leads to their third characteristic: all models are wrong … Models are wrong because they simplify. They omit details. By considering many models, we can overcome the narrowing of rigor by crisscrossing the landscape of the possible. To rely on a single model is hubris. It invites disaster … We need many models to make sense of complex systems. Yes indeed. To rely on a single
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Lars Pålsson Syll considers the following as important: Economics, Theory of Science & Methodology
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Why all models are wrong
Models share three common characteristics: First, they simplify, stripping away unnecessary details, abstracting from reality, or creating anew from whole cloth. Second, they formalize, making precise definitions. Models use mathematics, not words … Models create structures within which we can think logically … But the logic comes at a cost, which leads to their third characteristic: all models are wrong … Models are wrong because they simplify. They omit details. By considering many models, we can overcome the narrowing of rigor by crisscrossing the landscape of the possible.
To rely on a single model is hubris. It invites disaster … We need many models to make sense of complex systems.
Yes indeed. To rely on a single mainstream economic theory and its models is hubris. It certainly does invite disaster. To make sense of complex economic phenomena we need many theories and models. We need pluralism. Pluralism both in theories and methods.
Using ‘simplifying’ mathematical tractability assumptions — rational expectations, common knowledge, representative agents, linearity, additivity, ergodicity, etc — because otherwise they cannot ‘manipulate’ their models or come up with ‘rigorous ‘ and ‘precise’ predictions and explanations, does not exempt economists from having to justify their modelling choices. Being able to ‘manipulate’ things in models cannot per se be enough to warrant a methodological choice. If economists do not think their tractability assumptions make for good and realist models, it is certainly a just question to ask for clarification of the ultimate goal of the whole modelling endeavour.
The final court of appeal for models is not if we — once we have made our tractability assumptions — can ‘manipulate’ them, but the real world. And as long as no convincing justification is put forward for how the inferential bridging de facto is made, model building is little more than hand-waving that give us rather a little warrant for making inductive inferences from models to the real world.
Mainstream economists construct closed formalistic-mathematical theories and models for the purpose of being able to deliver purportedly rigorous deductions that may somehow by be exportable to the target system. By analyzing a few causal factors in their ‘laboratories’ they hope they can perform ‘thought experiments’ and observe how these factors operate on their own and without impediments or confounders.
Unfortunately, this is not so. The reason for this is that economic causes never act in a socio-economic vacuum. Causes have to be set in a contextual structure to be able to operate. This structure has to take some form or other, but instead of incorporating structures that are true to the target system, the settings made in mainstream economic models are rather based on formalistic mathematical tractability. In the models they often appear as unrealistic ‘tractability’ assumptions, usually playing a decisive role in getting the deductive machinery to deliver precise’ and ‘rigorous’ results. This, of course, makes exporting to real-world target systems problematic, since these models – as part of a deductivist covering-law tradition in economics – are thought to deliver general and far-reaching conclusions that are externally valid. But how can we be sure the lessons learned in these theories and models have external validity when based on highly specific unrealistic assumptions? As a rule, the more specific and concrete the structures, the less generalizable the results. Admitting that we in principle can move from (partial) falsehoods in theories and models to truth in real-world target systems do not take us very far unless a thorough explication of the relation between theory, model and the real world target system is made. To have a deductive warrant for things happening in a closed model is no guarantee for them being preserved when applied to an open real-world target system.
If the ultimate criteria for success of a deductivist system are to what extent it predicts and cohere with (parts of) reality, modern mainstream economics seems to be a hopeless misallocation of scientific resources. To focus scientific endeavours on proving things in models is a gross misapprehension of what an economic theory ought to be about. Real-world economic systems do not conform to the restricted closed-system structure the mainstream modelling strategy presupposes.
What is wrong with mainstream economics is not that it employs models per se. What is wrong is that it employs poor models. They — and the tractability assumptions on which they to a large extent build on — are poor because they do not bridge to the real world in which we live. And — as Page writes — “if a model cannot explain, predict, or help us reason, we must set it aside.”