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Michael Agar @alcaldemike

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October tuneup

I just returned from a workshop on complexity theory in San Antonio. The workshop was designed for health services researchers from Veterans Administration hospitals all over the country. I'd worked before with the two organizers and they asked me to be one of a number of faculty who would plan the workshop through conference calls and then attend to give presentations and work with small groups to help them process the paradigm shift that we wanted them to go through.

This isn't the time or place to go on and on about complexity theory. Increasing numbers of people do. For my money, Waldrop's book from the 1990s, called Complexity, is still a good way to sample the water. There are plenty of other resources, including MOOCs. The linguistics police should fine whoever came up with the name. The mathematical term for complexity is "nonlinear dynamic systems." When people hear complexity, they think "complicated." However, there are extraordinarily simple complex systems that produce extraordinarily complicated patterns.

For now, let's just say that complexity science is very different from the tradition of experimental laboratory-based linear causal research. That tradition is the one that drove Enlightenment science and became the "gold standard" of methodology, the one that does so poorly with social research questions around the actual – as opposed to laboratory – lived experience of human subjects.

A couple of things were particularly interesting to me during the workshop. One has to do with some old concepts in human social science, concepts like "declarative versus procedural." related to another less jargony distinction, "knowing that" (~declarative) versus "knowing how" (~procedural). It refers to two kinds of memory, one sort of like a dictionary or encyclopedia, the other sort of like an ability to do things, practical action.

The workshop participants, a smart group of researchers, got declarative complexity pretty quickly. But those same participants had been doing their research for many years under the threatening storm clouds of peer review based on the laboratory gold standard. So when it came time to talk about their own research, procedural knowledge kicked in and they thought in terms of methodologies deeply embedded in their memory for how to do science the right way. The problem is, you can't do complexity science only with, or even mostly with, gold standard methodologies.

Another interesting thing was the social version of the problem, called the et cetera principle by Harold Garfinkel. It goes like this. When someone describes how to do something, and you think you understand it and then set off to do it by yourself, things will come up in the task that you don’t know about and don't know how to handle. This is because the experienced person, who explained things, has subsumed many of the details of the task into a large “etcetera,” things that he knows how to do and assumes that anyone else would know, and possibly things that are so habitual that he isn’t aware of them himself. In this case, the problem is going from procedural to declarative without the hands-on experience that an apprentice role would provide.

One of many ways that this difference came up in the workshop was in the question of, “Where in the hell is the equation?” Most traditional research will use an equation to produce summaries and inferences based on calculation over some quantitative data set. A complexity science model may include procedures like that as well. For example, I used a power law distribution to set up the initial conditions of a social network for an epidemiology model. And I had to figure out ways to translate propositions about lived experience into a form understandable by the computer, which I thought of as "quantification as a form of translation" rather than "measurement." The resilient normal curve even played a part in setting up the initial risk proclivity of agents in the model.

But the overall picture of complexity science isn't about the initial conditions of a model. It's about all the surprising things a system might do over time given its contingencies and connections. The point of it isn't to develop the right formula to predict the future. The point of it is determining the space of possible paths that the system might take to learn what critical interactions, parameters and contingencies explain why one path might occur rather than another for different runs of the system.

The workshop succeeded in getting a large number of health services researchers at the Veterans Administration interested in a new framework for making sense of important health and organizational problems that the VA faces. And what I’m describing here could just be a version of the normal group learning process when a different way of thinking is on offer. And God knows I should’ve come clear on these issues long ago, given the many different kinds of workshops I’ve offered over the years.

I suppose I’m writing like I always do, to make sense of something I found interesting. In this case, I chalk it up to not being a participant and having a lot of faculty doing most of the work so I had the luxury to observe and listen carefully rather than having to run the show. Besides, I’ve been fascinated since I got into the business at how behavioral social researchers don’t realize that what is known in our disciplines applies to us and our research projects as well. Anthropologists call it “reflexivity” and in fact that topic came up at the very end of the workshop, at which point they handed the microphone to me, the anthropological faculty, so I could make something up about how things change when the researcher and the object of research were of the same category of phenomenon. We probably were all part of the same “good wave,” buena onda as they say in Spanish, a suitable way to close for a workshop in San Antonio. And I learned something about how to do workshops a little bit differently, probably just catching up with the ideas of Dewey and Vygotsky, something I should have learned long ago.

Life is interesting.

Selected Works

Wonder why studies you read about your world usually don’t get who you are and how you really live? Frustrated that “the numbers” don’t solve the problem? Does it bother you that policies and programs, more often than not, don’t work like they’re supposed to? People, organizations, countries–they rely on information about real human social lives. Usually they don’t have it because they only test what they think they already know in narrow situations of their own design. The results have value, some of the time, but it’s not nearly enough. We need a human social science that begins and ends in the real worlds of the humans that it claims to be about. One has been around for a couple of hundred years. The Lively Science tells the story of its historical roots and the reasons for its neglect, blends in new intellectual tools, and argues that it’s time to get on with a science that changes research objects into human subjects and learns who they are and what they’re trying to do before conclusions are drawn.
Living in a world of linguistic and cultural differences
A personal story of decades of work in the substance abuse field, a story of how our ineffective drug policy came to be and stayed in place. Now available as an e-book at iBook on iTunes and on Barnes and Noble.
The story of the working world of independent truckers in a time of deregulation
Nonfiction, Introductory Text
An introduction to ethnography