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Academic Insights for the Thinking World

Simplicity in a complex world

There is a polarization in management and policy thinking.

On the one hand, there is an increasing focus, for organizations, on defining detailed rules, standardizing methods, evidencing and measuring outcomes. The intention is to make the hospital, school, or firm work as an efficient, optimized, well-oiled machine. The belief is that if we tell people exactly what to do and check they do it exactly, then standards and efficiency will improve.

On the other hand, in the field of economics, there is almost the opposite – increasing deregulation and laissez-faire driven by a strong belief in the invisible hand of the market and in the power of competition to lead to optimal outcomes. And, although not necessarily obvious, in the field of economics too is the implicit assumption that, statistically, the future can be predicted, that policies can be designed and optimized. This unregulated economic world is still modelled as if it worked predictably and controllably, moving inexorably towards equilibrium.

What is remarkable is that these beliefs seem to harden and become evermore entrenched despite the repeating crises facing our economies, ecologies, and societies. They persist in spite of the stark and sometimes completely unexpected social eruptions and political crises that dominate the news. If ever there were a need for fresh thinking we are seeing it now. Yet most of the solutions that are attempted consist in propping up the status quo, doing more of the same, just trying harder—rather than thinking afresh and questioning underlying assumptions.

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Umeda Crossing by Strevo. CC BY-NC-ND 2.0 via Flickr.

What is less obvious perhaps, is that each approach gains its legitimacy from theories of physics – Newtonian, machine thinking leading to a plan-do-review method for management and thermodymanics leading to a ‘free market’ approach for economists. The question of the validity of attributing such scientific theories to the social world has long been questioned. In a world that is increasing complex, turbulent and global, do we need to seek new paradigms and perspectives?

Complexity theory is the theory of open systems; open systems, like organisations or economies, how they interact and are affected by the wider world. Traditional physics theories, in contrast, gain their tractability through acting as if they are closed and self-contained. Complexity gives us a different approach to engaging with the world – a middle ground between control and chaos. It advocates more tentativeness and less hubris. It suggests ‘try it and see’ or ‘try several things and see’ rather than ‘the analysis of the situation shows this is the right way to tackle it’. It implies that, whilst we should work as best we can to use evidence and information to inform our decisions, sometimes the information which captures the past quite well is of limited use in illuminating the future.

Complexity theory tells us we need to be wary of assuming everything can be tested and evidenced, and imagining that we can always know what caused what or what might happen next. We need to be more willing to experiment, to fail, and to learn from those failures—without giving up on clarity, and without abandoning the use of concrete data to plan and measure within limits.

What is easy to miss in saying all this is that embracing complexity can actually makes things easier, simpler, and more straightforward. How much time gets spent by organizations making cases, forming detailed plans, completing analyses, and demonstrating outcomes? How much of this really gets to the heart of the situation and really determines either what to do or what has been done? Perhaps less planning but more experimentation would be not only more effective but also simpler. Perhaps more focus on the initial selecting of good professionals, allowing them more autonomy to respond more effectively to the situations they are facing, would be less time consuming than the considerable efforts put in by managers to direct, measure, and control their performance. If the world is complex, then acting congruently with that complexity can be simpler than trying to control a machine that does not exist.

Headline image credit: 5/52 Mechanical by Jayp0d. CC-BY-NC-2.0 via Flickr.

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