The sensation is made so much more extreme when life-changing decisions need to be taken. Parents want to know that they are taking the best long-term decisions for their children. Farmers want to know that the money they borrow to develop their farms or plant a crop will produce a return. Investors want to know that their assets are safe from expropriation by politicians. Politicians want to know that they will not lose their seats (and salaries) in future elections.
Just like smokers who succumb to the craving for a cigarette, when people succumb to the craving to know what lies in wait over the horizon they start forecasting. This can be very dangerous, as a forecaster is doing something quite extraordinary − he or she is saying that one particular set of circumstances will coincide with a particular point in future space and time. In two articles published in the Harvard Business Review in the 1980s, the forefather of modern scenario planning, the Frenchman Pierre Wack, explained that forecasting is highly dangerous because forecasts are often right. However, Wack wrote that they are only right because the world they were based on has not yet changed. When that world does change, precisely at the moment when the forecast would have been most useful, it is useless, and the forecaster has to go back to the drawing board and start the process again, inevitably setting himself or herself up for more failures. Wack worked for the oil company Shell, where he shot to global prominence when he accurately anticipated the oil price spike of the early 1970s, which caught the rest of the global oil industry largely off guard. He also has a tie to South Africa in that he helped to train the scenario planning team at Anglo American, which similarly shot to fame under Clem Sunter in the 1980s with the High Road−Low Road scenarios.
There is now a body of theoretical research that proves that the futures of countries and economies can never be accurately forecast. This is the theory of systems, in particular an offshoot of that theory called complex systems theory. It is all very straightforward and easy to understand. Complex systems theory states that a typical complex system will display four characteristics:
Firstly, it is made up of a great many participants or actors that exist within the system. An ant colony would qualify. Weather and traffic patterns are complex systems. It is easy to see how South Africa (or any country), with tens of millions of people, tens of thousands of businesses, all manner of interest groups and a host of other actors, would qualify on this characteristic.
Secondly, these participants interact with each other within the system in pursuit of their goals. Ants do this. Climatological forces do, and so do cars and drivers in the morning traffic. In the case of an economy, the competition between businesses for customers is an example of such interaction. So too the efforts of competing activists or political parties. Every individual’s pursuit of wealth and happiness is an example of such interaction.
These participants direct what is called ‘feedback’ into the system – this is its third characteristic. Participants that are satisfied with their progress in the system direct a type of feedback that seeks to maintain the status quo of the system. Participants that are unhappy direct a different type of feedback, which seeks to change the system. It is easy to identify this behaviour within a country. When university students went on the rampage at South African universities in 2015 and 2016, stormed the Union Buildings, and broke through the gates of the parliamentary precinct, that was an example of feedback that sought to change South Africa’s status quo. Efforts to bring interdicts against the students and the deployment of the police on campuses were examples of attempts to maintain the status quo. In any system (or country), change happens when those actors seeking to change the system introduce a degree of feedback that overwhelms those that seek to maintain the status quo.
Finally, a complex system has a fourth attribute in respect of the interaction between its various participants; this is what is called an emergent characteristic. What this means is that the result of that interaction will be greater than the sum of its parts. Take the example of these simple equations below.
| Imagine that there are five participants in a system (we will call it system X). If a participant is happy with the status quo of that system, he or she will contribute a nominal value of 2 to the system. If they are unhappy they contribute a 1.In that case, where every actor in the system was happy, the system would look as follows:System X is 2+2+2+2+2=10Now let us imagine that one of the actors becomes unhappy and contributes a 1 to the system. In that case, the system would look as follows:System X is 2+2+2+2+1=9The system has changed from a ten to a nine – a significant change but not a dramatic or earth-shattering one.Let us now imagine that system X is a complex system that multiplies, instead of adding together, the feedback exerted by its participants. Where all the participants were happy the system would look as follows:System X is 2x2x2x2x2=32Now see what happens when one actor becomes disillusioned with the system and seeks to change the status quo:System X is 2x2x2x2x1=16The system has changed from a value of 32 to a 16 – a dramatic change. |
A good example of the emergent property of complex systems in action is the traffic. Many of us have to struggle through the traffic every morning. Thousands of other motorists struggle with us as we all compete to get to our destinations. If we all co-operate, the traffic might flow predictably if slowly. However, if just one driver breaks out of the status quo and causes an accident, she or he can trigger gridlock, which delays thousands of other motorists. They in turn delay many more thousands of other people who are waiting for them in meetings and places of business. Deals can be lost, money can be made and lost – all because of the act of just one participant in a system of thousands of others, and there is nothing that the thousands can do to change that.
It is that emergent property of complex systems that makes any attempt at long-term forecasting in a complex or volatile environment very difficult. You might as well try to forecast traffic patterns for your commute tomorrow morning. To do that, you would need to anticipate and account for the future actions of every other driver on the road. It cannot be done and, therefore, even if your forecast is ‘right’, this would only be because the world you are forecasting has not yet changed.
Now the Arab Spring can be better understood as a consequence of the emergent property of complex systems, as this explains how Mohamed Bouazizi in Sidi Bouzid was able to set in motion events that had such an extraordinary effect on the world. His example highlights perfectly the futility of trying to forecast, to a single point in space and time, the long-term future of any economy, country or region of the world.
We are faced therefore with a conundrum. On the one hand we have to account for the human craving for certainty about the future. On the other hand we have to work within the constraints imposed by the emergent property of complex systems. The solution lies in scenario planning.
Many clients are surprised to hear that scenario planning is not forecasting. The two methods are quite distinct. Whereas the forecaster seeks to define precisely a single future point in space and time, the scenario planner is seeking to identify a number of equally plausible points in future space and time. The distinction is that to the forecaster the future is a singular concept – there can be only one future and his or her job is to identify it. For the scenario planner, the future is a plural concept. There will always be more than one plausible future, and each of these must be respected as having a roughly equal degree of plausibility. It is only by accepting this that it becomes possible to overcome the crippling effects of the emergent property of complex systems.
At this point more than one client has suggested that it all sounds a bit hopeless. What is the point of identifying a series of roughly equally plausible futures? How can a decision be taken? What should she or he tell the shareholders and the board? That client has just run into reality and is now in danger of succumbing to the temptation to start forecasting.
Fortunately there is an acceptable compromise.
Our first answer to such a client will be that