Rivers as Complex Systems
Nikki Strong, St. Olaf College
I am an Earth scientist. I work on cross-disciplinary research (quantitative field, experimental, and theoretical) that applies the discipline of morphodynamics (how landscapes evolve in response to the erosion and deposition of sediment) and stratigraphy (how those morphodynamic processes are preserved in the geological record). My research as well as my teaching focuses on understanding and finding solutions to pressing present day and paleoenvironmental issues. I have worked mostly on fluvial systems, their dynamics and how they shape the Earth's surface. For my PhD I worked together with a team of engineers and geoscientists to design, run, and analyze data from a large complicated experiment that examined fluvial landscape response to changes in sea level, climate, and tectonics. It is some of these experimental data sets that I bring to this workshop as examples of complex systems. Rivers like all natural systems are inherently complex. They are systems comprised of numerous interconnected components that both self-organize as well as respond to outside perturbations in complicated unpredictable ways.
Thoughts on Teaching
I think that the greatest challenge in teaching and learning about complex systems is reducing complex systems into simple components that are both understandable and tangible, i.e. translating complexity into not just an abstract idea, but rather a useful tool for understanding our natural world. For example, describing a system as having fractal (self similar) behavior may be intellectually interesting but at the same time seem useless if we can not also explain dynamically what causes a system to demonstrate that fractal behavior. But not knowing why a system is fractal does not take away from the fact that one can use the fractal nature of a system to predict its behavior. I think sometimes that we forget that there many systems for which we can predict their dynamical behavior very well, even though we have no idea at a finer, more detailed scale what drives that behavior. Newton's law of gravity is a good example. It enabled us to land an astronaut on the moon, even though we have know idea how gravity actual works!
My favorite tools for deconstructing complexity are dimensional analysis, scale modeling, and frequency analysis. Most often I use the concept of scale to convey a sense of how to decompose a complex system into simpler parts. Also recently I have initiated a project with a fellow St. Olaf faculty member where we are translating complex signals/patterns in natural systems into sound. I think that this is a very intuitive way to 'understand' complexity.
I hope to learn how others have approached teaching the concept of complexity. It is a topic near and dear to my heart.
I believe that understanding 'how one learns' is invaluable, is a critical tool, in designing an effective learning environment. As a faculty member teaching and a researcher working on this topic, I am very curious to know what the 'cognitive underpinnings' of understanding complex systems are!