Policy making and analysis would be easier if cause and effect could always be clearly linked and understood in human systems. Small changes would have small effects, large changes would have large effects, what worked in the past would work in the future, and so on. Unfortunately, the world is much more complicated than this. Sometimes a small event can cause huge changes (e.g. the assassination of Archduke Ferdinand precipitating WWI), and sometimes a huge effort seems to make little difference (e.g. the US "war on drugs"), or even ends up moving the problem in wrong direction (the CAFE standards for automobiles and light trucks). To understand these all too common conundrums, we need to move beyond the simple, linear models which underlie much formal policy analysis to understand the implications of strategic behavior, non-linear feedbacks, and heterogeneous actors on our attempts to have a positive impact on the world of human activity.
This course will take a hands on, yet not mathematically formal approach to this material. We will begin with a review of the work of Tom Schelling in applying game theory to matters both mundane and profound. We will build on that foundation with readings that use agent based modeling, systems dynamics modeling, and network analysis to gain insight into human systems which display the kinds of emergent and unpredictable behavior which is typical of complex systems. By the end of the course, students should have a good sense of what can be done with the various tools of complexity theory and, more importantly, the ability to bring a complex systems perspective to bear on problems of real policy importance.