Explaining Complexity and Dynamic Systems Theory (CDST)

This month we published Research Methods for Complexity Theory in Applied Linguistics by Phil Hiver and Ali H. Al-Hoorie. In this post the authors explain why their book is so important for complexity research.

What are the big questions that occupy researchers in the human and social sciences? Chances are that these questions share two key features. First, many social questions, from the minute level to the grand scale of things, are interconnected. Second, their optimal solutions are constantly changing over time. As the late theoretical physicist Stephen Hawking once said, the 21st century is likely to witness a general intellectual reorientation around a complex, interconnected, and dynamic view of the world, a view that is indeed sweeping through various human and social disciplines. And, if many of the major issues of our time are complex and systemic, they need to be approached with a corresponding shift in perception. One such approach is complexity and dynamic systems theory (CDST).

Of course, once we began to adopt a CDST understanding of language learning, development, and use in our work in applied linguistics, it seemed to us that everything straightforward was ruined. Like many others, we had happily operated on the assumption of a neatly ordered and simple world. We studied phenomena by breaking them up into smaller parts, drawing boundaries between those parts, and studying them separate from their environment and in isolation. It is no wonder that before long we ended up frustrated and puzzled as to why we were no closer to understanding and capturing reality than before. While embracing a CDST view promised to bring us closer to an approximation of this complex and dynamic reality, we quickly realized that there was very little guidance for the methods necessary to do this kind of research. Many sources of information were too abstract or conceptual, but also misleading (e.g. “qualitative data are inherently better for studying complex systems”); others were far too technical (e.g. “Lyapunov functions are scalar functions that can be used to measure asymptotic equilibrium in stochastic models”) and did not seem to lend themselves to the kinds of questions that concern us applied linguists.

Methods for doing CDST research did prove elusive at first. But with just a little more digging, we became convinced that certain existing research templates, techniques for data elicitation, and methods of analysis that have a firm complexity basis in other human and social domains did hold promise. This book is the result of that journey we took to learn about already well-established designs and methods for complexity research. Based on our search, and a healthy dose of trial and error, we set out to share a variety of methods for complexity research already in widespread use by social complexivists. In the end, this is the book that we wish we had when we set out nearly a decade ago to explore the issues and questions of interest to us in applied linguistics. We hope it will function like a road map in pointing the way forward to many others who are also interested in the interrelated and dynamic reality of the human and social world.

For more information about this book please see our website

If you found this interesting, you might also like Profiling Learner Language as a Dynamic System edited by ZhaoHong Han.

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