complex technical tools such as cluster analysis, ultra metrics, and dimensional analysis.
This increasing complexity will be accompanied by a division of labor—theorists and
statisticians will be become more and more specialized, but they will be complemented
by economists who have a broad overview of where economics is going, and are trained
in applying economics. Economics will stop trying to answer grand questions such as is
the market preferred to command and control, or is the market efficient, and answer
smaller questions such as what structure of market will achieve the ends that policy
makers are trying to achieve.
Since the term complexity has been overused and over hyped, we want to point
out that our vision is not of a grand complexity theory that pulls everything together. It is
a vision that sees the economy as so complicated that simple analytical models of the
aggregate economy—models that can be specified in a set of analytically solvable
equations—are not likely to be helpful in understanding many of the issues that
economists want to address. Thus, the Walrasian neo-classical vision of a set of solvable
equations capturing the full interrelationships of the economy that can be used for
planning and analysis is not going to work. Instead, we have to go into the trenches, and
base our analysis on experimental and empirical data. From there we build up, using
whatever analytic tools we have available. This is different from the old vision where
economists mostly did the opposite of starting at the top and then built down.
The complexity vision is not only what we believe connects the various research
threads that will be the future of economics; it is also what we believe provides the best
way to look at the economics profession itself—we see the economics profession as an
evolving complex system that has competing forces operating at all times. It is a