Can We Reduce Complexity?

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A sketch of social complexity (by Phil Hawksworth)

Is it possible to make the complex simple? That was the subject of a conference call that I was a part of last week involving researchers and organizational change practitioners from around the world. The purpose of the call was to explore the potential for creating a conference that would address this very issue.

The call convenors were Eugen Oetringer and Dave Snowden . Like any topic worth spending time on, there was much debate on the very topic itself (and wide agreement that a conference framing the debate was a good idea).

The question of reducing or simplifying complexity is an important one for those trying to use complexity science methods to address wicked problems. The reasons are many. As a teacher (and always a learner) of complexity science and systems thinking methods and theories I can attest to the difficulty that people have with the subject matter. The reasons are also many.

First, in cognitive terms, the brain has a difficult time with processing multiple things at the same time. Research on cognitive complexity points to “chunking” as a promising means of supporting the necessary parallel processing of information necessary to make sense of complex information. The aforementioned citation by Halford, Wilson and Phillips (1998) nicely points to ways in which cognitive scientists define complexity:

Complexity is defined as the number of related dimensions or sources of variation. A unary relation has one argument and one source of variation; its argument can be instantiated in only one way at a time. A binary relation has two arguments, two sources of variation, and two instantiations, and so on.

In many complex problems, there are multiple arguments in play and no clear sense of what argument (if any) explains the problem in full. Much like Buddhist concepts of skillful and unskillful actions, complexity science deals with arguments that are more or less appropriate, not good or bad.

A second reason is that complexity is inherently mutli-disciplinary in its orientation. That is, the knowledge required to address problems of a complex nature cross many boundaries and it is rare if not impossible that one party will have a complete understanding of the situation. This requires that we problem-solve using not only multiple actors with different backgrounds, but multiple means as well. As the quote attributed to Albert Einstein illustrates:

We can’t solve problems by using the same kind of thinking we used when we created them.
Requiring different perspectives, and a diversity of tools, necessitates that there be some manner of engaging this diversity in a meaningful way. This is where we get social complexity. It is here that things often break down. The means of putting together individuals, ideas, and strategies from different backgrounds with different mental models of the way the problem is structured and about the landscape in which the problem occurs is probably the biggest challenge facing the task of complexity reduction.To reduce complexity, there is some need to get on the same page about what makes a problem complex, what elements exist within it, and how those elements are related before one can reasonably hope to make sense of what those patterns of relations actually mean, let alone devising a strategy for intervening.
Terms like “collaboration” are as commonly used as “innovation” and “networking” without much attention to what they mean at a fundamental level. Who among us in the academy, scientific, business or non-profit community would claim not to be innovative, networked or collaborative? My guess is few. Yet, the nature of what these terms means is critical for understanding the potential for creating strategies for addressing complex problems — let alone implementing and evaluating that strategy.
So: can we reduce complexity? The answer will depend on whether we can hope to organize ourselves in a manner that allows us to answer that question in the first place.

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