by The problem of reductionism in economics The kinds of laws and relations that economics has established, are laws and relations about entities in models that presuppose causal mechanisms being atomistic and additive. When causal mechanisms operate in the real world they only do it in ever-changing and unstable combinations where the whole is more than a mechanical sum of parts. If economic regularities obtain they do it (as a rule) only because we engineered them for that purpose. Outside man-made “nomological machines” they are rare, or even non-existent. Unfortunately that also makes most of the achievements of contemporary economic theoretical modelling — rather useless. When mainstream economists think that they can rigorously deduce the aggregate
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The problem of reductionism in economics
The kinds of laws and relations that economics has established, are laws and relations about entities in models that presuppose causal mechanisms being atomistic and additive. When causal mechanisms operate in the real world they only do it in ever-changing and unstable combinations where the whole is more than a mechanical sum of parts. If economic regularities obtain they do it (as a rule) only because we engineered them for that purpose. Outside man-made “nomological machines” they are rare, or even non-existent. Unfortunately that also makes most of the achievements of contemporary economic theoretical modelling — rather useless.
When mainstream economists think that they can rigorously deduce the aggregate effects of (representative) actors with their reductionist microfoundational methodology, they have to put a blind eye on the emergent properties that characterise all open social systems – including the economic system. The interaction between animal spirits, trust, confidence, institutions etc., cannot be deduced or reduced to a question answerable on the individual level. Macroeconomic structures and phenomena have to be analysed also on their own terms.
I concetti di eterogeneità e interazione portano necessariamente alla questione della non linearità. Si dice che un sistema è lineare se può essere suddiviso in più parti che sono autonome l’una dall’altra, cioè indipendenti. Quando invece le varie componenti interagiscono direttamente tra loro, parliamo di non linearità. Il comportamento emergente di un sistema è dovuto alla non linearità. Le proprietà di un sistema lineare sono davvero particolari: l’effetto di un insieme di elementi è la somma degli effetti considerati separatamente, e nel suo insieme non appaiono nuove proprietà che non sono già presenti nei singoli elementi. Ma se ci sono elementi combinati, che dipendono l’uno dall’altro, il complesso è diverso dalla somma delle parti e compaiono nuovi effetti.
Mainstream macro models describe imaginary worlds using a combination of formal sign systems such as mathematics and ordinary language. The descriptions made are extremely thin and to a large degree disconnected to the specific contexts of the targeted system than one (usually) wants to (partially) represent. This is not by chance. These closed formalistic-mathematical theories and models are constructed for the purpose of being able to deliver purportedly rigorous deductions that may somehow by be exportable to the target system. By analysing a few causal factors in their “macroeconomic 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. Or as Mauro Gallegati puts it:
Benché il riduzionismo abbia avuto successo in un gran numero di situazioni in fisica – dal nucleo dell’atomo, al moto di un razzo nello spazio, alle galassie – la sua validità non è universale … L’interazione tra elementi ci obbliga a passare dal riduzionismo all’olismo, e quindi a cambiare metodo … Come la biologia, e a differenza della fisica che cerca di semplificare i fenomeni con l’obiettivo di formulare leggi generali, l’economia dipende da specifici eventi storici e poiché la capacità di apprendere dall’esperienza è fondamentale, l’economia studia sistemi complessi evoluzionistici di tipo adattativo.
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 these macroeconomic models are rather based on formalistic mathematical tractability. In the models they appear as unrealistic assumptions, usually playing a decisive role in getting the deductive machinery 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 generalisable the results. Admitting that we in principle can move from (partial) falsehoods in theories and models to truth in real world target systems does not take us very far, unless a thorough explication of the relation between theory, model and the real world target system is made. If models assume representative actors, rational expectations, market clearing and equilibrium, and we know that real people and markets cannot be expected to obey these assumptions, the warrants for supposing that conclusions or hypothesis of causally relevant mechanisms or regularities can be bridged, are obviously non-justifiable. To have a deductive warrant for things happening in a closed model is no guarantee for them being preserved when applied to the real world.