The Economy as an Evolving Complex System III (2006) Eds Lawrence E Blume & Steven N Durlauf, OUP
The Origin of Wealth (2006) Eric D, Beinhocker, HBS and his talk at the Smith School, posted here: http://www.smithschool.ox.ac.uk/economics-and-public-policy-2/
Podcasts from the James Martin 21st Century school: Prof Geoffrey West, Santa Fe Institute, available from podcasts.ox.ac.uk
Beinhocker refers to complexity economics as paradigm shifting: i.e. not a 'how to do' but a 'how to think.' Starting from this premise, he then proceeds to outline the history of economics as a discipline, reliant on physics for its initial mathematical development. Unfortunately as physics developed in line with our better understanding of the physical environment and processes that occur across scales and made assumptions accordingly, economics failed to remould the assumptions upon which it was formulated. These 'laws' then became as ingrained as Newton's and Einstein's laws of physics, yet the fundamental principle of system equilibrium upon which they are based failed to take into account the 2nd law of thermodynamics (the tendency to entropy (disorder) in isolated systems). The assumption that the economic system is a closed system tending to equilibrium underpins key problematic laws in economics. Those relevant to my work on the food system include: the law of supply and demand driving towards market equilibrium price despite the evident existence of stocks, backlogs, time lags etc... and the law of 1 price where in the absence of transport costs and trade barriers, identical products must sell at the same price in all markets, which is very problematic when not using aggregate data (i.e. looking at the local level/fine-grained analysis). (Beinhocker notes that the more interesting question is actually that of price convergence and dynamic interplay over time between incentives for decision making and the changing nature of various barriers.
Beinhocker refers to complexity economics as paradigm shifting: i.e. not a 'how to do' but a 'how to think.' Starting from this premise, he then proceeds to outline the history of economics as a discipline, reliant on physics for its initial mathematical development. Unfortunately as physics developed in line with our better understanding of the physical environment and processes that occur across scales and made assumptions accordingly, economics failed to remould the assumptions upon which it was formulated. These 'laws' then became as ingrained as Newton's and Einstein's laws of physics, yet the fundamental principle of system equilibrium upon which they are based failed to take into account the 2nd law of thermodynamics (the tendency to entropy (disorder) in isolated systems). The assumption that the economic system is a closed system tending to equilibrium underpins key problematic laws in economics. Those relevant to my work on the food system include: the law of supply and demand driving towards market equilibrium price despite the evident existence of stocks, backlogs, time lags etc... and the law of 1 price where in the absence of transport costs and trade barriers, identical products must sell at the same price in all markets, which is very problematic when not using aggregate data (i.e. looking at the local level/fine-grained analysis). (Beinhocker notes that the more interesting question is actually that of price convergence and dynamic interplay over time between incentives for decision making and the changing nature of various barriers.
An interesting footnote is that he calculates that if the economic system were tending to equilibrium, this would take approximately 4.5X10 to the 18 (a few quintillion) years, makes you think, wot)
Furthermore, this fixation on equilibrium has resulted in various other problematic assumptions including rationality, perfect information and treating endogenous factors as external to the system. Rather, by relaxing some of these to include dynamism and less than perfect information/rationality/markets, the explanatory value of economics can be harnessed (rather than its predictive ability which is shaky at best and which is actually only 1/2 of the usefulness of models). By testing hypotheses based on what we see- i.e. with actual data that is not aggregated- the explanatory power of certain models can be tested in a scientifically rigorous way. Obviously the critical aspect of this is to collect the requisite data in the first place and to start testing theories rather than just statistical correlation (á la econometrics) which does not lead to conclusions of causation.
Complexity economics is therefore not the antithesis of neoclassical economics, but rather a re-evaluation of the mathematical principles upon which economics is based in order to bring them in line with current knowledge through the sciences on systems in general. By being able to analyse the economic system (a social system) in a manner similar to that in which natural systems are studied is the first step in being abe to research socio-ecological systems like the food system. Points from complexity science to bear in mind when trying to bridge this divide:
Complexity is scale dependent (We know the equations governing the solar system, yet it is extremely difficult to model a forest ecosystem in its entirety)
Networks recur at all levels across all systems: E.g. neural networks, business organisations, economies, trading regimes, social/community networks...
So, I'll leave you chew over that for now- I promise a part 2 (and maybe 3, 4... to follow :-)
L
