The more I practice futures, the more I have come to believe that simpler approaches work best. This is for two main reasons.
- Although the future is complex, and there are multiple possible futures, the ability to act on the future depends on being able to have a shared story about the challenges and the opportunities represented by the future. It is not enough to have knowledge of some ideas or images about the future, if you have them on your own. Social groups and institutions have to be able to align around a shared view if they are to respond effectively.
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Although the future is generally regarded as uncertain, large parts of its terrain are relatively certain. Over the timescale under consideration (which for most organisations is a future that typically runs between five and 15, sometimes 20, years) there are elements that are unlikely to change significantly: in 2030, or 2035 the population outside of sub-Saharan Africa will still be ageing.1
The scenarios gap
I’ve done dozens of scenarios projects, using a wide range of methods, and some of these have worked well. But–no matter how well you explain things, and no matter how transparent your processes are, there’s always a gap between what the scenarios tell you about a range of possible futures, and the implications to be derived from that scenario set.
Of course, you’re not supposed to pick one–you can’t control the future, even if you can influence it–but in the face of this gap, it’s not surprising that clients try to do this.
And the other reason this matters is that if futures processes are going to have an effect on an organisation–if futures thinking and futures-derived insight is going to create meaningful difference–the people who have been involved in the process need to be able to explain the process and the thinking to those who have not. The knowledge must be able to travel and circulate.
Complex knowledge
And the reason that this represents a difficulty in scenarios processes is because they are still typically driven along by a series of client workshops and working sessions. This isn’t because futures is particularly slow at picking up digital tools (although I have heard this case made). It is because, generally, scenarios and strategy work are about developing rich and complex forms of knowledge, and this requires rich and complex forms of human interaction. But that is a post for another day.
One of the tools I use to help people get a quick fix on the future is a schematic that I call “the futures diamond.”
I owe a partial debt to Jim Dator here. Some years ago I read a paper by him that argued that significant change (by which he meant generational-scale change) derived from three main sources. These three were, generations (or cohorts), technology, and economics. Thinking about this I realised that the “generations” tag conflated two separate sources of significant change: demographics, on the one hand, and values on the other.
So my first futures diamond was made up of demographics, economics, technology and values. But it is 2019, and it is pretty clear that a significant source of change is now resources and climate change. But when you look at this again, you realise that this is actually a product of the other four. In fact, the IPAT model of environmental impact maps onto the three elements at the topof the diamond. My conclusion: that Resources and Climate Change sits inside the diamond at the intersection of its four corners.
What story does this tell us about the way the world is changing?
Slowing down
Well, one of the things it tells us is that rather than speeding up, in important respects things are slowing down.
In demographics, the rate of population growth is slowing. Peak baby was in 1992, and peak workforce was in 2012. The number of people of working age globally is going to continue to decline. One implication: countries with low levels of economic participation (for example because women are discouraged from working) wil either have to automate or enroll these people into their labour markets.
In economics, the overall rate of growth has been in decline for fifty years, although it has only recently reached levels where it starts causing political problems. Even in Asian markets growth is slowing. At the same time, productivity is stuck. The easy gains in economic growth from population increase are gone (see Demographics, above).
In technology, for all the noise about AI and machine learning, the work of Carlota Perez suggests to us that the speed we have seen is a product of a particular period of a very familiar technology pattern. We are reaching the end of the third part of a new technology S-curve, when growth (and profits) are fastest. Digital markets are saturated in the majority of countries worldwide. And so on. At the same time, there is credible evidence that many of the rapid gains made from AI are because you always get rapid gains early on; in machine learning, the code being generated by the ML process may not be properly understood. Increasing complexity leads to mistakes, as the Boeing crashes suggest.
The values transition
Values is a different story. We are in the middle of a long secular transition in values, as Richard Inglehart suggests. Across North America and much of Europe, we are at the moment of a tipping point where the post-materialist generation (values associated with autonomy, diversity, and self-expression) are becoming the majority, and the modernist generation (values associated with hierarchy, authority, and conformity). These transitions are always destabilising, and it may be that this is an explanation for the populist surge we’re seeing everywhere: the modernists’ last stand.
And finally, climate and resources. A crisis, but also an opportunity: HSBC tells its clients that the investment needed to retool our economies so they don’t trash the planet is around $100 trillion over the next 15 years. $100 trillion over 15 years. (Why we aren’t using the free capital with which the world is currently awash is a mystery, partly explained by the persistence of belief systems among social groups such as economists well beyond the point at which they are still useful as a means to explain the world).
These last two are going to be where the energy for social and economic innovation comes from.
There’s a bit more to this. The interplay between the factors in the bottom half of the diamond (Economics, Technology, Values) also has things to say about business models and infrastructure, and therefore, potentially, about transition and socio-technical regime change, but that’s for discussion another time. I sometimes use this model when someone needs a quick snapshot of the drivers shaping their environment. It works at a global level, but it also works for countries, or regions, or business sectors.
The image of Earth at the top of the post is courtesy of NASA.
- One definition of a “relatively certain” outcome is that if it becomes uncertain over the time period we’re looking at, it will probably mean that we’re no longer going to be so worried about the project question. For example, if we’re no longer living in an ageing society in 2035, we may be more worried about the locations of the plague pits. ↩
the next wave 
The Diamond model is a helpful framing, thanks for sharing. A question – where would “policy” as a prime dimension fit? There’s typically an interplay/alignment of markets, technology and policy required for large changes.
Hi Scott, Going back to the source idea, about factors that create change on generational timescales, that tends not to be policy. But the interplay between economics, technology and values influences both infrastructure and business models, which are second order effects. I suspect that policy also plays out at this level, to influence particular types of infrastructure preferences or to encourage particular business models (defined broadly).