The design needed and practiced in government departments and the broader public sector is often of a systemic nature. Typically, planned actions require a substantial period of time for implementation and the actual outcomes from these actions can only be observed after an even longer period. This is the case in education, healthcare, economic development, social change, legislative reform, to name the most obvious.
Such long-term plans are vulnerable to changes in the objectives and priorities of the institution that are prompted by changes of government, environmental conditions of implementation (e.g. economic fluctuations) as well as changes in needs and wants of the target users. How can systemic designs be made more resilient against these changes?
I want to make the case that Strategic Foresight can be a very useful tool in policy and program planning, but especially in improving the resilience of systemic designs.
First, let’s look at long-term forecasting, which is essential in any public sector venture. We know it is not easy — at one time or another famous prediction statements by luminaries of their time that were shown later to be completely wrong (see the sidebar).
The people making these predictions were not uneducated people. On the contrary, they were leaders and pioneers in their industries and businesses and were often leading-edge scientists, engineers, and executives in their domains. So why were they so terribly off in their predictions and why does this pattern of knowledgeable people making very wrong predictions persist?
A first hint can be found in Ray Kurzweil’s How to Create a Mind. In his research on the processes of the human brain, Kurzweil found that our neo-cortex features hierarchical pattern recognizers. It therefore acts as an “innate built-in linear predictor.” Consequently, the human brain fundamentally favors linear projections, including projections of the future. Basically, we assume that things will evolve as they did before and the forecasts made by prominent people focused on developments within complex systems such as technology, economy, and society.
There’s a new way of thinking, and already there is a substantial body of literature studying the nature of such systems, not only in science and technology but also in social sciences, humanities, and management science. Consider for example the following book titles:
- Nonlinear Dynamics in Economics,Finance and Social Sciences;
- Chaos and Complexity Theory for Management;
- A Thousand Years of Nonlinear History;
- Political Complexity: Nonlinear Modelsof Politics.
All of these learned works demonstrate an awareness that the complex systems we are predicting for are non-linear systems.
The gap between the linear projections that our brains favour (even when we are told about our bias) and the actual developments that occur in a non-linear fashion is what is are called “strategic surprises.”
What have we done to mitigate the uncertainty caused by this gap? For natural systems we have mandated our scientists to construct sophisticated non-linear models that help us predict future behavior. So, for example, the weather forecasting models or the climate change models are fed huge amounts of measurement data (temperature, precipitation, wind direction and speed, ocean temperature, etc.) captured by a broad range of technologies (satellites, airplanes, balloons, radar, ocean probes, terrestrial measuring stations etc.).
With the improvements of computer processing capabilities new systems are gradually coming of age and yielding reasonable predictions for the short- and medium term projections. There are very few models that can provide long-term predictions with sufficient reliability but here too we are making progress, albeit more slowly. As with many other models, predictions by linear projection can be very close to the nonlinear model predictions for the short term. As the prediction timeframe increases the differences between the linear projection and the nonlinear model become increasingly visible.
For a host of other systems, in particular social and economic systems, we have not been able to build models of sufficient fidelity and accuracy. Think about the 2008-2009 economic downturn that no economic model predicted. One main reason for our modeling shortcomings, if not outright failure, is that the uncertainty is mainly caused by human behavior. While we can predict with great accuracy the number of youth or seniors in a particular geography in the year 2035, we cannot predict what their behavior will be like. That is because behavior is driven by a large number of factors including economic, political, environmental, and cultural factors that cannot be easily modeled.
In order to mitigate uncertainty in these important systems a school of thought emerged that is based on the following premises:
- There is no data about the future. Any measurement is by definition in the present or past. Linear extrapolations can be useful for short time horizons but cannot be used for longer time horizons.
- Long-term uncertainty can only be reduced by improving preparedness for multiple possible futures.
- Futures built around our critical uncertainties are the most useful as they help reduce uncertainty in areas we know the least about.
- Strategic foresight helps us systematically build multiple futures using our critical uncertainties.
A useful definition of Strategic Foresight is the one offered by Richard A. Slaughter of Foresight International in Brisbane, Australia: “Strategic Foresight is the ability to create and sustain a variety of high-quality forward views and apply emerging insights in organizationally useful ways.”
Shifting from a singular future mindset to a multiple-futures mindset is the most fundamental shift required to apply Strategic Foresight to systemic design, particularly designs requiring a long span of time to be implemented and show first results.
In these cases that wish to forecast over a long period of time, there is a higher probability that the environment in which the new design will be activated will be significantly different from what was initially projected. If the conditions assumed for successful implementation or the objectives of the institution or the needs of target users have changed during that long span of time, then the implementation may not achieve its objectives and even fail completely. This possibility makes the system being changed vulnerable.
Strategic Foresight can be used to improve the resilience of the system by using a multiple-futures approach to develop a future-proof design. Foresight offers many methods that can be used to explore plausible and possible futures. Assuming that the 2×2 matrix method was selected, the process would follow the standard approach to developing multiple scenarios for the desired time horizon.
This would involve collecting tangible signals of change, in particular weak signals that might indicate an emerging phenomenon; analyzing the signals for trends; determining the main drivers of these trends; identifying the most critical drivers, whose development within the study’s time horizon is very uncertain; and using the top two critical uncertainties to construct four scenarios.
Each of the top two critical but uncertain drivers are given two extreme and opposite status: For example, a best-case at one end and a worst-case status at the other. The combination of two uncertain critical drivers, each with two opposite values, provides four possible pairs of drivers. Each combination is used as the key drivers for one scenario (Figure 2).
Once the four scenarios have been fully developed, they provide a granular view of the complexity and interdependencies in each world. It becomes then possible to consider and articulate the implications for the project under the very different meta-conditions of each scenario. These implications can in turn be used to develop mitigations strategies and modifying the original design to make it more resilient to the various possibilities of the future.
The modified design can also be validated through the so-called “wind tunneling” process. Wind tunneling, a concept originally developed by the aerospace industry, consists of running the modified strategy through each of the four scenarios to understand its strengths and weaknesses in each situation. If significant weaknesses are uncovered another cycle of testing, evaluation, and design modification is implemented until the weaknesses are deemed manageable. The outcome of this process is a significantly more resilient design compared with the original one.
By applying a strategic foresight approach to systemic designs, the public sector can thus deploy more resilient solutions that can better withstand a range of possible futures. In addition, the foresight process helps the design collective gain a deeper understanding of the correlation between critical drivers and their implications, which enables it to more efficiently monitor key events and hence detect earlier the direction of changes. In summary, a strategic foresight approach strengthens the strategic capabilities and stance of the designing collective.
Nabil Harfoush, PhD, is a member of the Resilience Design Lab, OCAD University