Peter Radford — Why Mainstream Economic Models Make Little Sense

My criticism of the use of models in economics is then not one of a denial of the value of models. It is directed at the selection of them. More to the point it is directed at the severity of the exclusion of zones of our experience that fall outside of our theoretical study because those zones are less tractable to our modeling technique. We have become monotheistic in our beliefs. Polytheism is frowned upon.

This is despite one of the great lessons of evolution: that diversity of approach gives the greatest chance of the location of solution. Economists have limited their options by reducing their subject to that set of issues most easily modeled according to the discipline’s self-referential code of modeling. And, further, they describe the problems they set out to solve in such a way as to produce results from their tests that conform to a prior code of acceptability.

So the code of modeling limits the usable technique, and the code of acceptability limits the range of problem. This duality then combines to define the discipline. Economics thus becomes defined as the subject that theorizes about problems tractable to the preferred technique.…

Evonomics
Why Mainstream Economic Models Make Little Sense
Peter Radford

Lars P. Syll — Macroeconomic challenges

It would be better to just face the truth — it is impossible to describe interaction and cooperation when there is essentially only one actor.

Macroeconomic challenges
Lars P. Syll | Professor, Malmo University

The assumption of representative agents is atomistic in the sense that atoms are indistinguishable elements of a homogenous system that is characterized by timeless invariant relationships that can be captured as mathematical functions — as it physics. However, social systems are not at all like physical systems, are they? 

So why assume atomism in the first place, when it is bound to fail. The only realistic answer I have seen is methodological convenience. Such models are tractable. Modeling complexity is hard.