Modern economics is obsessed with modelling. An overwhelming majority of academic papers on the subject work like so: they take data, and use data to construct formal mathematical models of economic processes. Models mostly describe a situation, and describe how that situation would be changed by a given set of events; a very simple example is that as the supply of a good diminishes, its price will increase. Another is that deficit spending increases the national income. A mathematical model is a predictive tool created to demonstrate the outcome of events in a massively simplified alternate universe.
As someone who rather enjoys voyages of the imagination, the use of mathematical models in economics is intriguing. The pretension that through using formal mathematical techniques and process we can not only accurately understand, but accurately predict the result of changes in the economy is highly seductive. After all,we can accurately predict the future, right?
Wrong. The wonderful and terrible and confounding thing about our world is that it is a deeply unpredictable place, at least in the economic sphere where each number (for instance “aggregate demand” or “aggregate supply”) in an equation may loosely refer to millions of huge, complex and dynamic events. When you’re using huge simplifications to describe reality, those simplifications may miss the important details, and your projections may go askew.
Not all modelling is equal. Newton’s model of gravitation (since superseded by Einstein’s relativity) makes relatively accurate predictions about how gravitation works, and what would happen to an object dropped 500 metres above the Earth. NASA used Newton’s equations to fly to the Moon. Of course, even in physics there are occasionally divergences and oddities (which is why there are quite often unrepeatable or anomalous experimental results, for instance the recent experiment that seemed to show neutrinos travelling faster than the speed of light). So economics — with its fixation on creating models of situations, and using these models to attempt to predict the future, mimics physics, chemistry and biology, where data is collected, and used to produce theories of physical processes which allow a modestly accurate representation of the future.
The key qualitative difference, though, is that mathematical economic theories don’t accurately predict the future. Ben Bernanke — the chairman of the Federal Reserve, and one of the most-cited academic economists in the world told the world that subprime housing was contained. That is the economic equivalent of Stephen Hawking telling the world that a meteorite is going to miss the Earth, when it is really going to hit. Physicists can very accurately model the trajectories of rocks in space. But economists cannot accurately model the trajectories of prices, employment and interest rates down on the rocky ground.
The thing that I believe modern economists are most useful for is pointing out the glaring flaws in everyone else’s theories. Steve Keen has made a public name for himself by publishing a book entitled Debunking Economics, in which he explains the glaring and various flaws in modern economic modelling (DSGE, New Classical, etc).
Economics is a complex and multi-faceted subjects. Economists must be in some measure, philosophers, historians, linguists, mathematicians, statisticians, political scientists, sociologists and psychologists, and many other things. The trouble is that at some stage in the last century the multi-faceted multi-dimensional economics (like that of Xenophon) was hijacked by mathematicians who tried to turn this huge and delicate subject into an equation. Yet economics — and economic decisions, from the macro to the micro level — is a human subject. It is subtle and psychological and sporadic. A human subject requires human language, human emotion, human intuition.
The grand theoretical-mathematical approach to economics is fundamentally flawed. Trying to smudge the human reality of economics and politics into cold mathematical shackles is degenerative.
So what to do if you want to understand the economy?
Follow the data, consider the history (similarities and differences between the past and the present) and explain your conclusions simply, as you would to a child. Consider philosophical definitions: what is money? What is demand? What is supply? What is value? How does demand affect supply? What are the global patterns of trade? Why have they emerged this way and not an alternative way? Consider possibilities. Admit the limitations of your knowledge and explore the boundaries. Stop forcing the construction of absolutes, grand frameworks, grand theories. No theory will ever be robust to everything nature will throw at it, but simple microeconomic heuristics (opportunity cost, cost-benefit analysis) combined with data-focussed historical analysis may be more robust than cold, dead mathematics.
As Heraclitus noted:
No man ever steps in the same river twice
No two situations are identical. And in this universe even tiny differences can have huge effects on the outcome of a situation. This is the butterfly effect, a term coined by Edward Lorenz, and derived from the theoretical example of a hurricane’s formation being contingent on whether or not a distant butterfly had flapped its wings several weeks before.
The pseudo-scientific school of mathematical economics hungers and craves for a perfect world, where each river is the same, where there is no butterfly effect, where human preferences are expressed in equation form, where there is no subtlety or ambiguity or uncertainty.
It is a dreamworld constructed by and for people with Asperger’s Syndrome.