The perennial curmudgeon H.L. Mencken is famously misquoted as saying: “For every complex problem there is an answer that is clear, simple, and wrong.” The ability to simplify is of course one of our strengths as humans. As a species, we might just as well have been called homo reductor—after all, to think is to find patterns and organize complexity, to reduce it to actionable options or spin it into purposeful things. Behavioural economists have identified a multitude of short-cuts we use to reduce complex situations into actionable information. These hard-wired tricks, or heuristics, allow us to make decisions on the fly, providing quick answers to questions such as ‘should I trust you?’, or ‘Is it better to cash in now, or hold out for more later?’ Are these tricks reliable? Not always. A little due diligence never hurts when listening to one’s gut instincts, and the value of identifying heuristics is in part to understand the limits of their usefulness and the potential blind spots they create. The point is, there is no shortage of solutions to problems, whether we generate them ourselves or receive them from experts. And there’s no dearth of action plans and policies built on them. So, the issue isn’t so much how do we find answers?—we seem to have little trouble doing that. The real question is, how do we get to the right answers, particularly in the face of unrelenting complexity?
There’s a nomenclature in the hierarchy of complexity as well as proper and improper ways of going about problem-solving at each level. This is presented in the new publication “From Transactional to Strategic: Systems Approaches to Public Challenges” (OECD, 2017), a survey of strategic systems thinking in the public sector. Developed by IBM in the 2000s, the Cynefin Framework posits four levels of systems complexity: obvious, complicated, complex and chaotic. Obvious challenges imply obvious answers. But the next two levels are less obvious. While we tend to use the adjectives ‘complicated’ and ‘complex’ interchangeably, the framework imposes a formal distinction. Complicated systems/issues have at least one answer and are characterised by causal relationships (although sometimes hidden at first). Complex systems are in constant flux. In complicated systems, we know what we don’t know (known unknowns) and apply our expertise to fill in the gaps. In complex systems, we don’t know what we don’t know (unknown unknowns) and cause and effect relations can only be deduced after the fact