Complexity and Keynes’ General Theory
In my studies I will be exploring how theories of complexity can be connected to post-Keynesian economics and, ultimately, Cambridge Econometrics’ E3ME model. Like any new student, my first task is a big pile of reading. Near the top of the pile is a return to Keynes’ General Theory.
The General Theory was written a long time before modern theories of complexity were developed. However, there are not really glaring inconsistencies or incompatibilities in the text. It is true that Keynes does mention equilibrium a lot (something regarded as a lifeless planet by complexity scientists) but usually in the sense of something dynamic that the economy moves towards, rather than attains (p343). This passage from Chapter 18 (p247) is certainly consistent:
Our present object is to discover what at any time the national income of a given economic system… which means in a study so complex as economics in which we cannot hope to make completely accurate generalisations…
In fact, Chapter 18 follows a thread that would resonate with complexity scientists, for example discussing how the economic system could become “violently unstable” under certain conditions. True, he does not mention power laws or use statistical distributions, but this is only consistent with another mention of the word complexity in Chapter 21 (p278) that reflects Keynes’ well-known views on mathematics:
Too large a proportion of recent “mathematical” economics are mere concoctions, as imprecise as the initial assumptions they rest on, which allow the author to lose sight of the complexities and interdependencies of the real world in a maze of pretentious and unhelpful symbols
This argument against mainstream neoclassical economics is still made to this day.
More generally, there are themes in the General Theory that complexity scientists would recognise throughout. Keynes is very clear about the need for a distinction between micro and macro (p85; p293), which is the divide that complexity economics and agent-based models seeks to address. Given some of the discussion in Chapter 18, it seems likely that Keynes would have been fascinated by these types of interactions.
Some of the other themes are better known. Fundamental uncertainty is a core underlying principle of Keynesian economics and is also a key feature of complexity. Evolution is not discussed explicitly but is an implicit assumption in the economy never reaching the equilibrium at which point transactions would cease.
Related, one area that is not covered much in the General Theory is the role of innovation and technology as driving the evolution of the economy. Often the state of technology is taken as given (although not necessarily constant) in order to simplify the analysis. A basic description of process innovation is given on p271 but less is said about the development of new products, which is of more interest from an evolutionary perspective. For this it may be necessary to turn to the works of Schumpeter (more on this to come).
Similarly, Keynes does not talk about networks and how knowledge/behaviour may percolate through the economy. Although he recognises “psychological elements” that could be “subjected to further analysis” (and of course lots on expectations, e.g. Chapter 12), there are relatively few clues as to the nature that might take.
Overall, it seems reasonable to conclude that Keynes’ thoughts in the General Theory with regards to complexity are reflected in post-Keynesian economics today. He acknowledges that the economy is a complex system but does not attempt to assess the complexity involved. This contrasts with mainstream neoclassical economics, which denies the existence of complexity altogether through assumptions of perfect knowledge, homogeneous agents, etc.
Writing more than 80 years ago, Keynes did not have the tools to assess complexity in the economy. Now such tools are available and this is the essence of what my studies will look at over the next five years. We observe complexity in the economy every day and society’s greatest challenges (e.g. financial stability, climate change, rapid automation) will all require complex systems thinking. Sadly, this quote from p33 of the General Theory is as relevant today as it was then:
For professional economists, after Malthus, were apparently unmoved by the lack of correspondence between the results of their theory and the facts of observation;—a discrepancy which the ordinary man has not failed to observe, with the result of his growing unwillingness to accord to economists that measure of respect which he gives to other groups of scientists whose theoretical results are confirmed by observation when they are applied to the facts.