from physics. The temperature of a gas increases because all the gas molecules rub against one another. In other words, the temperature changes because of the way in which each gas molecule interacts with all the others. Again, it would be futile to try to understand the rise in temperature by studying individual molecules in isolation. All a researcher would then see is a single molecule vibrating in a vacuum.
This work in physics and biology eventually led to the emergence, in the latter half of the 20th century, of what is known as complex systems theory. As its name suggests, it seeks to explain the behaviour of extremely complex systems that, it holds, have the following common characteristics:
They contain very large numbers of actors or participants.
Those actors or participants interact regularly with one another.
Through that interaction they direct feedback into the system based on how satisfied they are with their circumstances in that system.
There are two types of feedback. The first seeks to stabilise the system and maintain its status quo. This type of feedback is directed by actors or participants who are satisfied with their circumstances. The second seeks to change the system and is directed by actors or participants whose expectations are not being met.
The two types are in constant conflict in any system. Where the former predominates the system will remain stable and will not change. However, where the latter predominates the system will change – and often dramatically.
These interactions and the feedback they produce are emergent, which means that they produce a result that is much greater than the sum of its parts.
This last idea has been popularised as the ‘butterfly effect’, namely the notion that, when a butterfly flaps its wings on one side of the world, it could cause a storm on the other side.[5]
In my experience, the best example of a complex system, and the one we use most often during our scenario briefings to introduce our audience to complexity theory, is that of traffic in an urban environment. Every day tens of thousands of motor vehicles interact with each other on roads and highways as their drivers make their way to their destinations. We all know that it takes just one actor, such as a broken-down car, or an object lying in the road, to cause a stoppage or slowdown, which rapidly builds up into a traffic jam, and eventually into a total system failure known as ‘gridlock’. The knock-on effects are enormous, ranging from the human and material costs of accidents to huge sums in lost economic productivity. It would be futile to study each vehicle in a traffic jam to understand its cause. A traffic jam can only be explained by examining how the vehicles in question interacted with one another. Therefore, major traffic jams and their consequences are a perfect example of the emergent property of complex systems – or the butterfly effect at work.
At North West University, we developed the proposition that similar effects can be observed in any political or economic system. A small and seemingly insignificant change in the behaviour of just one actor in such a system can affect all the other actors in that system, and eventually change its future. We produced the following very simple equations to demonstrate this effect as it might play itself out in a political system:
Imagine that there are only three actors, or participants, in the South African economy, and assume that each contributes a value of 2 to the economy. If the economy was a simple system, in which participants made these contributions in isolation of one another, the system could be expressed by adding their contributions together, as follows:
2 + 2 + 2 = 6
However, in the case of a more complex system, the individual contributions or components would need to be multiplied, as follows:
2 x 2 x 2 = 8
Now consider what happens if at some point in the future one of the actors contributes a 1 and not a 2 to the system. The simple system would now look like this:
2 + 2 + 1 = 5
Here the outcome for the system has changed from a value of 6 to a 5. Had a forecast been made of the future of such a system, and the forecaster had been unaware that one of the actors in the system would no longer contribute a 2 to the system, the forecast would still have been reasonably accurate. However, the complex system would change radically, as follows:
2 x 2 x 1 = 4
Here, the outcome for the system has changed from an 8 to a 4. This time, had a forecast been made of the future of this system, and the forecaster been unaware that one of the actors would no longer contribute a 2, the forecast would have been totally wrong.
These calculations are perfect illustrations of the butterfly effect – in other words, that a small, seemingly inconsequential, change in the behaviour of even one actor in a system of millions of actors could dramatically change the future of that system. This is why no one can forecast what the traffic will be like at any given point in the future. The fact that the actions of just one vehicle or driver can completely thwart the plans of all other drivers makes such a forecast impossible.
John Kane-Berman is fond of telling a story about the former National Party and later Herstigte Nasionale Party politician Jaap Marais that effectively demonstrates the quality of emergence, or the butterfly effect, in a political context. In 1968 the then prime minister, John Vorster, agreed that the Springboks could play an All Black team that included Maoris. Marais warned him that such a compromise would one day cause ‘a black man to marry your daughter, and sit next to you in parliament’. His NP colleagues laughed this off, but Marais was actually right: in compromising once on the principle of racial separation, Vorster unintentionally contributed to a chain of events that would culminate in the ending of apartheid.
No forecaster could possibly have realised what the consequences of Vorster’s decision would be. Consider the thousands of significant decisions taken every year over the life of the apartheid system by a huge range of role players including the NP government, political movements in exile, internal resistance movements, civil society organisations, foreign governments and other international institutions, and many others, and one starts to realise why accurately predicting the future at that point was effectively impossible.
What our research at NWU revealed is that, in the case of complex systems such as political systems, the future can never be accurately forecast to a single point in time and space. Such forecasts are largely based on known trends, which are then projected into the future,[6] but even a seemingly insignificant shift in one of those trends can completely change its future trajectory.
We concluded that all the past failures of political and economic forecasting resulted from this problem.[7] It is not possible for any analyst, no matter how well-informed, to track every single shift in South Africa’s current political system – and therefore equally impossible for any forecaster to extrapolate current trends into a single accurate prediction of the future. For this reason we need to do what many scenario thinkers have done before, and that is to move away from the idea that a single pre-ordained future exists and rather reassess our view of the future and how we think about it.
Four types of futures
In his excellent book 20/20 Foresight: Crafting Strategy in an Uncertain World, the futurist Hugh Courtney comes up with the radical and brilliant idea that there are different variants or types of futures that are distinguished from one another by their relative uncertainty. He cites four such types, namely:
A ‘clear enough future’, when the range of possible outcomes is so narrow that uncertainty does not matter. This does not imply that such a future is perfectly predictable, but rather that it is predictable
