Читать книгу A Time Traveller's Guide to South Africa in 2030 - Frans Cronje - Страница 6

Humans do not like uncertainty, and medical researchers have discovered the reasons why. In his book On Intelligence, Jeff Hawkins (inventor of the PalmPilot) writes, ‘Your brain receives patterns from the outside world, stores them as memories, and makes predictions by combining what it has seen before and what is happening now. Prediction is not just one of the things your brain does. It is the primary function of the neo-cortex, and the foundation of intelligence.’

Psychologists say the craving for certainty is similar to cravings for oxygen or certain foods. Cigarette addiction would be a good example of such a craving. Humans are programmed to seek out certainty and then make decisions that are based on confidently knowing what will happen next. Denying the brain confidence in such certainty produces a physical response of great discomfort and even agitation – equivalent to what may happen if you deny cigarettes to a smoker. This is why the last nail-biting minutes of a very close rugby or cricket match produce such extremes of emotional behaviour – oscillations from elation to tears of despair and back again. During a briefing a client made the point that this is why train stations put up electric boards telling passengers in how many minutes the next train will arrive. It calms people on the platform and cuts down on aggressive and anti-social behaviour.

All of this presents a problem for economic and political analysts. If you think the anxiety around a rugby match is bad, try telling a retailer that it is unclear what consumer trends will do over the next quarter, or tell a room full of investors that currency trends have become difficult to predict. I have seen some corporate boards and strategists become quite emotional that there are scenarios that are at odds with their strategic plans. This is not that they disagreed with the scenario or challenged it on the basis of the facts or quality of argument – their response was neurologically driven, a natural human reaction triggered by the fear that comes with not knowing. It is a deeply troubling and uncomfortable sensation.

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 scenario planning projects are not going to throw up hundreds of different futures. Most scenario projects will deliver between two and four different future worlds. Our second answer will be for the client to develop a strategy for each world and then adopt the one that seems to align most closely with the current environment. Not for a moment, however, should that strategic decision be taken at the cost of jettisoning the other scenarios. The client should develop a series of indicators indicative of the emergence of each of the other scenarios and be prepared to turn on a dime the moment it seems that a new scenario has become most probable.

Take a practical example. Many clients ask advice on how ANC economic policy will change. A forecaster could provide an answer, and the client might use that answer to build a strategy for his operations in South Africa. A scenario planner would provide two or three different sets of answers, probably going as far as compelling the client to face up to the question of what economic policy would look like if the ANC were no longer around. A decision could then be made about which of the answers the current climate seems to suggest as the most probable, and the strategy that applies to that scenario can be put into operation. But when the climate changes – and it will – the client already knows exactly what to do, while competitors are scrambling to figure out what just happened and how to respond.

Does the singling out of one scenario not contradict the complex systems basis of scenario planning? To an extent it does, but reality leaves no alternative and as long as it is not done, with the result of writing off the other scenarios, it offers the best solution to the conundrum that exists between the prescripts of complexity theory and emergence, on the one hand, and the neurological craving to know what will happen tomorrow, on the other.

The differences between forecasts and scenarios are therefore the following:

Forecasts develop a single certain future around which a strategy must be built. There is not much early warning that the forecast may be wrong. There is no fall-back position or Plan B if it is wrong. New strategy must be developed in the midst of the chaos of change.

Scenarios usually develop two to four varied futures. A strategy is built around each future, and indicators are available to show which future is most likely to materialise. If the markers change it is easy to change strategy. In a well-built set of scenarios nothing should be able to occur that takes the company, country or government that commissioned the scenarios by surprise. They have a contingency plan for each eventuality. Somewhat counterintuitively, by agreeing to work within the constraints imposed by the emergent property of complex systems, the client who has agreed to accept the plural nature of the future actually has far more certainty about the future than the client who chooses to rely on a single forecast.

How do you create a set of scenarios? The methodology most commonly employed is tried and tested and has been used with variations by consultancies around the world for over 30 years.

The first step is to identify what the client actually wants to know, and over what time frame he or she wants to know it. In other words, what is the focal question and over what time horizon must the question be answered? The question can be very broad or extremely narrow. One request may be to know how banks might be exposed to land reform policy over the next three to four years. Another may be what the long-term (ten- to twenty-year) implications of current mining policy might be for greenfield mining exploration in South Africa. An activist group may want to know what the worst-case outcome for civil rights might be in order to test the likely efficacy of a contingency plan it had developed. A media company may want to know what the effect of ‘view on demand’ technology might be for traditional radio and tele­vision stations. In the case of this book we want to know what life will be like in the South Africa of 2030.

The second step is to identify every economic, social and political force that might have an impact on that decision. The net must be cast very wide, and several hundred indicators or pieces of information may be gathered. In the case of this book, four chapters will be devoted to explaining current economic, social and political trends.

The third step is to gather those trends into a series of groups or families of related major trends. Ideally we want to get down to 40 or 50 major trends, each of which will have a definitive influence on the question the scenarios seek to address.

The fourth step is to rank those trends according to the impact they are likely to have on the core question that the scenarios are trying to answer, and the uncertainty associated with that impact. This is done on a graph with two axes such as the one set out below. The left axis measures relative uncertainty and the bottom axis measures relative impact. Trends that are grouped towards the lower left corner of the graphic will have a relatively limited impact on the scenarios, and there is relatively little uncertainty about what that impact will be. Trends grouped towards the top right corner will have a relatively high degree of impact on the question the scenarios are trying to answer, and there is great uncertainty about what that impact will be. It is these types of trends against which business and political strategists need to test their contingency plans if they are to be confident that those contingencies can anticipate and respond effectively to sudden and dramatic shifts in the environments they operate in.

The fifth step is to determine what those sudden shifts are likely to be. This is done by taking the trend of greatest impact and plotting it against that of greatest uncertainty on a matrix such as that set out below. The matrix in turn delivers four quadrants, and each of these will become one scenario. The robustness of this methodology is that it takes the greatest uncertainties faced by an organisation and multiplies these by the trends that will have the greatest impact on that organisation. To demonstrate how this works, the matrix provides an example of a set of mining and natural gas scenarios. That hypothetical study suggested that geological conditions would have the greatest impact on the future of the mining industry in South Africa, while mining policy was the greatest uncertainty faced by the industry. The matrix suggested that the likely best-case scenario for mining was Scenario 1 in which generous geological conditions coexisted with enabling mining policy. The worst case was Scenario 3 in which increasingly difficult geological conditions (very deep gold seams and limited natural gas reserves, for example) coexisted with a hostile mining investment climate.

With the matrix plotted, we now have direction. We know that the future will fall within one of the four quadrants of the matrix. But we do not yet know which one, nor do we know with precision what each of the four futures will be like. The latter problem is solved by going back to the original research conducted in step two and setting out how each of the trends identified in our original scan of the broader economic and policy environment would be likely to evolve in each scenario. In other words, we have to write a story of what life is like in that future. In a sense it is a fictional story because it has not yet happened. But the story will occupy a strange no-man’s-land between fiction and reality as it will be based on hard data and trends that we know are real and from which we can easily extrapolate. The aim here is to provide the sense of suspended disbelief that we are already in that future. That is very important because the emotions evoked must inspire the readers of the scenarios to act in the present to realise the best outcomes and avoid the worst. They must recoil in horror from the worst outcomes and work very hard at achieving the best. In this sense, good scenario sets often turn out to be self-fulfilling prophecies in that they inspire management teams, corporations or even countries to reach the best-case scenarios.

The final step is to identify a number of markers or indicators indicative of the likelihood of the current and most probable scenario changing to another. By tracking those indicators closely, there will be adequate advance warning of which scenario is going to happen.

At a recent investment seminar I set out four plausible outcomes for South Africa in 2024 and what their implications were for people with considerable savings and investments. A member of the audience, somewhat agitated, rose to enquire why I could not tell him which of the four would happen. I did tell him, but also recited all the warnings about the emergent property of complex systems and that, rather than obsessing over which scenario would materialise, investors might be better off accepting inherent uncertainty, developing an investment strategy for each scenario, and reading the environment very closely to shift strategy as circumstances in the country changed. Very few companies and organisations do that well, but it is essentially what sets strong strategic planning teams apart from weak ones and robust companies from less successful ones – especially those invested in volatile emerging markets.

Many of the companies we have worked with have found that developing a strategy for four scenarios is not actually much more demanding than developing a strategy for one. They can then confidently adopt the strategy that aligns most closely with the scenario they deem most probable. But they are securely positioned to switch strategy at a moment’s notice if there are sudden policy or market changes and another scenario becomes more probable. Compare their position with that of a company that rather chooses to rely on the single forecast of a consultant who has the confidence to say that he knows how the future will evolve. When markets shift and policies change, that company may find itself in a crisis. The forecast they choose to rely on has no value. They have to go back to the drawing board, do new research, and develop new strategies for the changed environment. However, the company that planned through scenarios will be many months, even years, ahead of them. The same applies to families planning for their futures in South Africa.

In this book we are going to follow the methodology given here. The outcome will be a set of precise descriptions of what life in our country will be like in 2030. Each will be accompanied by five easily identifiable indicators of which scenario it will be. The result will be to provide more than a decade of advance warning of where we are all headed – more than enough time to make a plan to capitalise on the best that South Africa will offer and hedge against the worst. There will be no excuse to say we did not see it coming.