Complex Adaptive System Science

In my current faculty position, I teach a “complexity” course to undergraduates called “Systems Thinking”.  In addition, I continue to stay active in the complexity programs at ASU (CAS@ASU) through the Center of Social Dynamics and Complexity and the Center for the Study of Institutional Diversity (see below for more information).  Previously, I served as the assistant director of Arizona State University’s Complex Adaptive Systems Initiative (CASI), which is a collaborative effort to leverage trans-disciplinary relationships to address complex global challenges in health, sustainability, security and education by creation of entirely new technologies and novel solutions. This requires integration of diverse research disciplines across the University and building an extended network of global collaborations.

Before describing what CASI is and what it does, it’s worthwhile answering the obvious question:

What is a complex adaptive system?

One definition is that a complex adaptive system is comprised of a heterogeneous and diverse network of interacting and independent actors that learns and adapts over time. The behavior of a complex system is often said to be emergent and subject to self-organization.  In short, the macro-level behavior of the system is more than the sum of the micro-level.

Complex adaptive system science then is a new approach to science.  Actually, I break the study of complex adaptive systems into three categories – an approach to science, a set of computational methodologies, and a series of metaphors that serve as means to help understand the world.  As an approach to science, studies in complexity take a more holistic view of science and generally move away from a typical reductionist approach.  What this means more generally is that, while a reductionist approach to science has served humanity well in understanding atomic physics, landing a man on the moon, and other feats of the (broadly) physical sciences, it does less well at many of the most pressing problems of humankind today.  Honing in with more and more detail does not help in understanding such diverse phenomena such as cooperation and competition in society, the workings of a market, or the interactions in a food web, among others.

By a set of methodologies, I refer to many of the new approaches to science that use tools such as agent-based modeling to stochastically study the emergent behaviors from the interactions of individual actors/agents over time.  Other similar methodological approaches would include statistical physics approaches to social science problems, dynamic modeling techniques, network analysis, and so on.  While these may not all directly tie to the definition of complex adaptive systems expressed above, users of these approaches often discuss their work in terms of complexity, as formally defined or not.

Finally, many complexity scientists use these terms descriptively, as metaphors, to explore and explain how micro-motives can lead to “emergent” macro-behaviors, the role of learning and adaptation in the evolution of systems, and how self-organization and bottom-up processes may result in very different outcomes than more hierarchically structured situations.

My current work with CASI takes a two-pronged approach.  Internal to ASU, we run a great deal of complexity science research through the Consortium for Biosocial Complex Systems, which is comprised of three independent centers:

1.  The Center for the Study of Institutional Diversity

2. The Center for Social Dynamics and Complexity

3. The Mathematical, Computational, and Modeling Sciences Center

Our internal focus is to provide research venues and outlets through these centers, to provide educational opportunities, and outreach on complex adaptive systems science.  The educational opportunities currently include a graduate-level concentration available to a wide-variety of majors that provides training in complexity and relevant methods.  We are currently expanding course offerings at the graduate and undergraduate levels with planned undergraduate certifications, minors, and multiple lines of study.  Our educational offerings will soon be expanding to include training workshops available for faculty, staff, and students interested in complexity.  The outreach component of our work includes a distinguished “Challenges in Complexity” Speaker Series.  Past and upcoming speakers can be found at our speaker link.

The second prong of CASI is externally focused.  These activities take two forms – developing partnerships with other complexity centers and institutes around the world and a series of research-oriented workshops.  The current list of partners is in the relevant links section below.

Relevant Links

My Published Research in this Field

See my publications page for access to these articles!

Baggio, Jacopo, Marco Janssen, Michael Schoon, and Orjan Bodin. (2001). “Landscape Connectivity and Predator-Prey Population Dynamics ”, Landscape Ecology 26, (1): 33-45.

Schoon, Michael L. 2011. “Commons Complexity and Understanding Transboundary Conservation Areas”. The Commons Digest Issue 10 edited by Alyne Delaney. 

Salau, Kehinde, Michael Schoon, Jacopo Baggio and Marco Janssen. (2012). “Varying Effects of Connectivity and Dispersal on Interacting Species Dynamics”, Ecological Modeling 242: 81-91.

Current Research Program

Schoon, Michael, Kehinde Salau, Jacopo Baggio, and Marco Janssen. “Modeling Decision-making across Habitat Patches:  Insights on Large-Scale Conservation Management”, under review with Environmental Modeling.

My research program in complexity, in conjunction with Kenny Salau and Jacopo Baggio, expands on the landscape dynamics and multiple, interacting species to encompass management regimes.  Similar to my work in transboundary conservation, this work models how manager interactions affect interspecies dynamics on a landscape.  Current research includes the addition of a single manager on a fragmented landscape, the addition of multiple managers each managing a patch on a fragmented landscape, and expansion to look at coordination and collaboration across a broader landscape.

This research ties in with the research programs on collaborative governance and attempts to provide management insight to practitioners based on modeling results.  Likewise, the modeling draws on data gathered in conjunction with the case studies of the collaborative governance projects.

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