Economics needs to treat the economy as a complex system

The path to better understanding the economy requires treating the economy as the complex system that it really is. We need more realistic behavioral models, but even more important, we need to capture the most important components of the economy and their most important interactions, and make realistic models of institutions. The complex systems approach is intermediate between traditional economic theory and econometrics. Traditional economic theory is top-down, modeling decision making from first principles, and then testing against data later. By “first principles” I mean the requirement that theories have “economic content”, i.e. that they derive behaviors from preferences. Econometrics, in contrast, takes a bottom up, data-driven, but fundamentally ad hoc approach. The complex systems approach sits in the middle, taking a bottom up data-driven approach that differs from traditional econometrics by explicitly representing agents and institutions and modeling their interactions, without the requirement that everything be derived from fundamental principles. It has the potential to free behavioralism from the straightjacket of equilibrium modeling, and to bring the computer revolution fully into economics. This path will at times involve abandoning economic content in favor of economic realism. 1 Linear mathematics and non-complex economics At the 2012 INET conference “Paradigm Lost: Rethinking Economics and Politics”, I was asked to speak in a session titled “Taking Stock of Complexity Economics: Which Problems Does It Illuminate?”. This title made me laugh because it reminded me of a lecture by the great mathematician Stan Ulam that