Learning amidst Complexity

 

In a previous blogpost I described three mindsets for systems change work: systems mindsets, complexity mindsets, and humanistic mindsets.  Here’s more on complexity mindsets.

In an article entitled Learning from Evidence in a Complex World John Sterman described the fundamental unpredictability of complex systems - such as world economies, community infrastructures, and public health – and the rise of unintended consequences resulting from well-intended, well conceptualized policy interventions.  He noted that traffic congestion has increased in areas that have been redesigned to reduce traffic, bacteria have flourished and become resistant to antibiotics developed to combat their spread, forest fires have become more intense in areas experiencing forest fire suppression techniques, and levee and dam construction has led to increased housing development in flood plains which can no longer serve to eliminate excess water and consequently more and more damaging flooding has occurred.  Sterman identified these unintended consequences as evidence of policy resistance in complex systems.  Those who wish to intervene in complex systems will be challenged to produce new and better outcomes due to the unknown and unknowable structure and behavior of complex systems, time delays, and the impossibility of identifying and controlling all relevant aspects of the system.

Instead, Sterman advises that moving toward better outcomes requires learning, specifically learning from evidence, which is particularly challenging for groups working on complex problems.  More clearly, Sterman writes, “Complexity hinders the generation of evidence.  And, “Complexity hinders the implementation of policies based on evidence.”  And so Sterman concludes that we need scientific methods and modeling techniques that allow us to do two things simultaneously: 1) explore the deeper assumptions and mental models that drive our policy decisions; and 2) learn from scientifically derived evidence about the impacts of our decisions.  Learning from evidence should be structured to allow for revision, even radical revision, of our initial assumptions and mental models.

“Creating a healthy, sustainable future requires a fundamental shift in the way we generate, learn from, and act on evidence about the delayed and distal effects of our technologies, policies, and institutions. The reductionist program of ever finer specialization is no longer sufficient. Though often leading to deep and useful knowledge, it contributes to policy resistance by narrowing the boundaries of our mental models.”  Sterman, 2006.



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