Understanding Complexity by Scott E. Page, Ph. D.

I recently completed Understanding Complexity from The Great Courses lectures produced by the Teaching Company which, as the title suggests, primarily concerns complexity science. The lectures were written and presented by Dr. Scott E. Page who has the fascinating title of “Leonid Hurwicz Collegiate Professor of Political Science, Complex Systems, and Economics” at the University of Michigan. So, how does one become an expert in complexity? Page completed his BA in mathematics at the University of Michigan, then an MA in mathematics at the University of Wisconsin, an MA in managerial economics from Northwestern University. He later earned a Ph.D. in Management Economics and Decision Science from Northwestern as well. I didn’t look up his dissertation but during the lecture series, he stated that his doctoral research was in game theory.

The course is divided into twelve 30-minute lectures and includes a course guide containing the professor’s additional notes and suggested further readings and resources. Since absolutely no one has suggested that I do so, I have decided to rank these source materials for SIP blog posts on a simple scale from 0 to 10, with 0 indicating I could find no redeeming value whatsoever in the course, lectures, book or other resource used as source material. On the other hand, 10 means I am prepared to form a cult based around these teachings. I would give the Understanding Complexity lectures a solid 7. 

Professor Page begins by making a distinction between system complexity and a system that is just particularly complicated. Four factors must be present to indicate that a system is complex: 1) It has a population of diverse agents that are 2) connected. They also exhibit behaviors and actions that are 3) interdependent and 4) they must demonstrate adaptation. 

One or the more insightful concepts of the course comes in the second lecture which describes evolutionary processes and the creation of diversity. In evolution new characteristics develop through mutation or sexual recombination. Since there is no intentionality in the process of evolution there is no bias for a particular search direction. Thus, evolutionary “search” takes place against the backdrop of an “evolutionary landscape.”

It is useful to think of each of the types of landscapes (simple, rugged, and/or dancing) as problems and the solution is to find the highest peak in a given landscape. Simple landscapes are like Mount Fuji—little variation of terrain, a steep slope straight up to a single peak. Rugged landscapes are like the Appalachian Mountains—there are many “local” peaks but finding the single highest peak will take some exploring and effort. Finally, a “dancing” landscape has local peaks and valleys (like its rugged counterpart) but it also changes with time. Dr. Page has us visualize being an extremely myopic hiker trying to find the global (or maximum) peak in the mountain and this serves as an allegory for evolutionary exploration. 

Now, I realize that the explanation I’ve given is neither clear nor concise but that is what’s great about Professor Page’s lecture series: He gives detailed elucidations with such clarity you almost think you understand the concepts until you start writing your blog posting and see that it was not as easy as he made it look. 

One last parting shot at communicating an idea that doesn’t seem like garbled lunacy. Emergence, in philosophy, systems thinking, and science, is how complex systems develop from numerous, much simpler component parts. An example of an emergent phenomenon from every day life can be demonstrated in the phrase “birds of a feather flock together.” Hundreds of birds follow simple, instinctive roles (maintain precise distance, stay aligned, avoid predators) and create this much larger, distinct thing: a flock. Dr. Page really got my attention when early on in the lecture series, he suggested that human consciousness might be an emergent property of the brain. No single neuron, synapse, or glial cell has any of the properties exhibited by the macro-level phenomena of human consciousness; however, the billions of these cells acting in concert do seem to be the building blocks of this emergent phenomenon that allows us to understand ourselves as a unique “I” operating at will within the world. 

The series touches on many other topics from several different domains. I think it is particularly useful for systems engineers and other engineers to keep this perspective about complexity. Unfortunately, this course is given in one of the shorter formats for Teaching Company lectures which was disappointing to me after I saw how enjoyable and applicable the series was. Part of this subject matter was germane to my dissertation in graduate school. Since then, I had lost a lot of enthusiasm for such topics and for decision science and operations research in particular. This series helped renew my interest and passion for the field. 

As always, happy learning! And keep pushing on!