I presented in a parallel session, the morning after the conference dinner, and had a predictably small audience. However that disadvantage had a fortunate side, because that tiny audience included the two conference organisers Louis-Philippe Rochonand Claude Gnos, as well as Basil Moore and Allin Cottrell. Basil is the venerable father of the proposition that the money supply is endogenously determined, rather than set exogenously by the Central Bank, as is still taught (in wild conflict with both the empirical data and actual Central Bank knowledge and practice) in almost all macroeconomics courses; Louis-Philippe and Claude are well-known and respected Post Keynesian monetary economists; Allin is a very capable exponent of Marxian economics, who unlike most Marxists uses computer modelling extensively in his analysis (I just wish he’d update his webpage, which doesn’t appear to have changed since 1997!).
The discussion was therefore possibly better than it would have been, had I presented in a plenary:
However though I was pleased with the way my paper was received by those present, I was very disappointed with most of the presentations at the conference. Though there were some notable exceptions–one of which I’ll comment on below–the papers were either non-analytic (“What Keynes said was…”, “Economists must take uncertainty seriously…”), bombastic (“The fatal flaw in the capitalist system is …”), or used graphical analytic methods that could not easily be distinguished from the content of an ordinary macroeconomic textbook. There were one or two block diagram expositions, but they too were graphical only–mere drawings, not influence diagrams, and certainly not systems dynamics models.
There are many leading Post Keynesians who weren’t at this conference–including quite a few who attended the Australian Society of Heterodox Economists conference that Peter Kriesler organises at much the same time every year–so I’m not claiming that the papers here are utterly representative of the general state of Post Keynesian economics today. Nevertheless, if they were even mildly representative of the work that Post Keynesian economists are doing in the midst of the biggest crisis that capitalism has faced in seventy years–and one which is causing a crisis in neoclassical economics as well–then they will fail to shift economic theory at all. After ten or fifteen years of economic pain, the neoclassical orthodoxy will be reassembled–since it will be true that “there is no alternative”–and Post Keynesians will remain a noisy and largely ignored minority.
Papers like these, though they are intended to criticise the unreality of neoclassical economics, or to point out issues (uncertainty, bounded rationality, open systems, non-ergodicity, whatever) that should be taken seriously in economics, actually strengthen the resolve of neoclassical economists to do nothing of the sort, since they lack any coherent alternative analytic approach.
Neoclassicals who attend such presentations–which almost always include disparaging remarks about the absurd assumptions neoclassical economists make–walk away quite justifiably thinking that “if that’s the best you can do with realism, then I’ll stick to my ‘absurd assumptions’!”
We can and must do better than that. But to do so, non-orthodox economists have to find tools that can express their vision of the economy analytically, either as mathematical or computer models. If we don’t, then whatever might be said by “Critical Realists” about the inappropriateness of mathematical analysis in economics, or how one can’t model open systems mathematically, the critics will be sidelined in a not too distant future by those who do use such models–and who care a good deal less about realism than the critics do. Yet again, the critics may win the philosophical battle, only to lose the methodological war.
That’s why I’ve put in the effort to learn the methods of dynamical analysis in mathematics (systems of differential equations), engineering (systems dynamics), and computing (multi-agent models), and it’s why I’m trying to develop alternatives to those which make sense in the context of economic modelling–notably my tabular method to develop systems models.
These dynamic models enable us to put our thought processes into a systematic framework, and to explore relations that are simply too complex to follow verbally. This is a major benefit to mathematical analysis that is lost in the critiques non-orthodox economists tend to make of how neoclassicals abuse mathematics: when we outline a causal mechanism verbally, we are in fact stating a differential equation verbally. If we say that “Factor X causes changes in variable Y”, we are actually saying “the rate of change of Y is a function of (amongst other things) Factor X”. In mathematical notation, this is d/dt (Y) = F(X).
The advantage of expressing these concepts mathematically, as well as verbally, is that the mathematical rendition keeps track of all the feedbacks and complex interactions that simply overwhelm our capacity to follow a complex causal process verbally, and they give us a means to provide a rough quantification of how strong those feedback effects are.
The failure to do this within Circuit Theory is why a simple confusion of stocks with flows–mistaking the stock of money for the flows that are initiated by a given stock of money over a year–has stymied for twenty years the development of Graziani’s brilliant insights into a workable theory. As I show in the talk above, the simple expression of the flows initiated by a loan are sufficient to solve all the “conundrums” of Circuit Theory. The conundrums were simply the product of applying the wrong type of analysis–simultaneous equations, “period analysis” with its implicit difference equation form, or worse still mere words–to the issue. A simple application of flow analysis in continuous time shows up all those conundrums for what they really are: confusions resulting from bad analysis and inappropriate analytic methods.
Now I also have to exhort my fellow Post Keynesians to learn at least some of the appropriate methods. Get out of the comfort zone of verbal exposition, historiography, simultaneous equations and graphical analysis–and even the much more sophisticated stock-flow consistent framework of Godley and Lavoie (While this method is certainly a major step in the right direction, using it to try to explain where profit comes from was rather like trying to understand how a horse runs, using photographs of a running horse taken at one hour intervals)–and learn differential equations, or systems dynamics, or computer programming. It’s hard, but the effort is worth it. And if you don’t do it, then prepare to once again be dominated by neoclassical economists once the Global Financial Crisis has passed.
I’ll end on one very positive note: there was one exceptional piece of work done by a PhD student (who is also a full-time school teacher) Pascal Seppecher. He has developed a multi-agent model in Java that also simulates the monetary circuit, and reaches much the same result as I do from a differential equations perspective. His model is called Jamel: Java Agent-based MacroEconomic Laboratory. It’s a brilliant piece of work and I do recommend exploring it.
If a full-time school-teacher with a family can nonetheless acquire the skills and find the time needed to do quality work like this, then it’s high time academic Post Keynesians did the same. Sticking with what you are used to, when what you are used to merely lets you point out what “should be” done rather than actually doing it, is no longer good enough.