Less Crystal Balls and More Applications, Please

The Cabreras introduce a plethora of new concepts in Section 2 of Systems Thinking Made Simple, including proposals for “cognitive jigs,” or structures of systemic thought. These content agnostic tools seem particularly applicable in designing the metacognitive maps they reference in Chapter 4. The jigs include:

• P-Circles (diagrams that include perspectives and viewpoints)
• Part-Parties (diagrams breaking down concepts/things into parts)
• Barbells (diagramming the relationships between two things
• R-Channels (connecting 2 systems of parts together via a channel)

I thought these were conceptually valuable, and I liked how the Cabrera draw a distinction between maps including such jigs and traditional system maps (which focus on information rather than structure).

In Chapter 6, the authors claimed their simple DSRP rules had predictive power. What seemed to be the most relevant example was the disciplinary nature of scientific community. They discuss how hyperspecialization within academic science has led to the unfortunate result of highly skilled specialists that are more focused on their individual field than overall problem solving. I agree that this is a systemic issue and that categorization plays a big role. But I’m not quite sure how the DSRP rules are supposed to have predicted that. In general, we know that social sciences are largely evaluated by their explanatory abilities rather than their predictive ones. Economic modelers have never successfully predicted upcoming recessions, and political scientists repeatedly fail to tell us who will win presidential elections. I certainly don’t mean to discount social sciences—I believe their role is just as valuable as natural sciences, despite their inability to predict. Instead, I’d just like some clarity from the Cabreras about how systems thinking (a discipline grounded in social sciences like sociology and psychology) could possibly be predictive, even with their simple rules. I found that section to be fairly inaccessible, so if anyone else understands it better and wants to counter-argue, I’d love to hear your thoughts.

Finally, the applications section seemed a little weak. Rather than applying the theory to relatable examples, the authors made the theories even more abstract by discussing how other academic theories had roots in DSRP. So my question this week is:

How do we apply any of the concepts from this chapter to relevant environmental issues?

Comments

[Erik]

I'm definitely in agreement with you here. I found the book's claim of DSRP's predictive power to be extremely suspect. From what I could tell the DSRP analytics are meant to predict the relations, perspectives, etc. that the system modeler missed. Which isn't to say that it's not a useful tool, but it is still limited by the modeler's interpretation of the system's scope.
I took issue with the examples as well. On the rare occasion that a real world example was used, it was extremely watered down. Everything in the last few chapters of our reading seemed to compound to bring doubt about DSRP's effectiveness.