Seeing Hard and Soft Trends
Foresight professional Daniel Burrus in Flash Foresight (2011) further divides probable futures into two useful general categories, hard trends and soft trends. Hard trends and what he calls “future facts” are things that seem very highly probable (90%+, a hard trend) or effectively certain (a future fact) to happen or continue to happen, as far as we can tell today. They are based on known science, causal models, or a history of highly predictable, measurable processes. The sun will rise tomorrow (a future fact). Automation will continue to accelerate (a hard trend). We’ll keep moving data and software to the cloud (a hard trend). Our cellphones will be able to listen in on and semantically understand our conversations within ten years (another hard trend), etc.
Distant future facts are less common, but they certainly exist. Our universe will have no more stars, or life as we know it, in roughly 10 trillion years, give or take a few trillion. The future timing of these predictions aren’t that precise yet, but the thermodynamics they are based on is so well evidenced today that we can call these future facts. For a fun big picture outline of some things history and science tells us about our past, and science tells us about our future, including the death of our sun and the universe, see Philipp Dettmer’s nice 7 minute infographic video, The History and Future of Everything, YouTube (2013). As the video describes, our sun will become a red giant in perhaps 5 billion years. Earth will be uninhabitable for biological life in around a billion years.
Not covered as a future fact, as it is today only a hypothesis, is the idea that postbiological (technological) life, if it emerges as many now expect, will not need to live on planets, and will be able to copy itself at will, making it effectively immune to destruction, unlike biological systems.
Our high-probability estimates could at any time be made wrong by hidden processes and incomplete or incorrect models, so we must always look for unexamined alternatives, qualifiers, uncertainties, and assumptions. Furthermore, since science and human knowledge are always imperfect, there will always be many more candidate hard trends than future facts. Nevertheless, it can be very helpful and clarifying to discover and improve our estimates of relevant high-probability processes and future events. Every hard trend or predictable future we find closes off certain doors to the future, and stops us wasting our limited time and energy on exploring those possibilities. At the same time, finding predictability opens other doors, and focuses us on processes and outcomes we can influence, including soft trends.
Soft trends also have a probability we can attach to them, but this probability is lower than for hard trends. The key difference is that these are processes or events we say or expect “might” rather than “will” happen. We usually also consider these trends to be subject to social influence and intervention, though sometimes, as for astronomical events, we talk about events that might happen where social influence is obviously not possible. At what specific probability our language or expectations change from “will” to “might” and from “inevitable” to “influenceable” for any trend or prediction will differ for different people. Timescales are important too. In the longer run, many hard trends turn soft. Less frequently, soft trends become hard too, as a system edges toward to some inevitable transition (say, the probability of India’s independence from Britain during the 1940’s).
For another example, many scholars expect our rapid exponential computer performance/price improvement “will” continue as a hard trend over the next five years, even though certain aspects of Moore’s law have been slowing since 2005, as we will see in Chapter 2. Most of these folks would expect that Moore’s law “might” continue, as a soft trend, over the next twenty years. Global average temperatures “will” rise at least a bit over present day temperatures in the next few decades, according to our best climate science. They “might” rise as catastrophically as six degrees in the next hundred years, as Mark Lynas explains in Six Degrees (2008). The world “might” continue to become measurably safer and less violent, on average, just as it has over the last twenty millennia, according Stephen Pinker, Better Angels of Our Nature, (2010).
Using good historical data and experiment to refine the probabilities of our hard and soft trends requires an evidence-based practice. The emerging discipline of cliometrics, for which Robert Fogel and Douglass North received a Nobel prize in 1993, is the probabilistic and predictive study of economic history, using statistical methods and theories from modern economics, and new methods of historical research. Advances like cliometrics tell us that as our world digitizes, both our histories and our futures will be much more predictive than they are today, in at least three ways. Our probable futures will be more specifically predictable. Our possible futures will be more broadly predictable, in terms of the expected range of variability. Finally, our competing preferable futures will be better mapped and communicated.
As Burrus says, all good foresight work should “Start with certainty.” We need to learn past, present and future facts, hard trends, and constraints that seem relevant to our problem. Once we know enough about our problem domain to find many of those, we have earned the right to think well about what might happen (soft trends). These are both examples of developmental foresight.
Once we have a good understanding of both high and lower probability developments, we are then in a good position to intelligently brainstorm and debate previously unimagined possibilities, unknowns, uncertainties, qualifiers, contingencies. These are all examples of evolutionary foresight. All this work in turn helps us define and pursue realistic preferences (visions, goals, strategy, plans, and actions), so we can better survive and thrive in the world. But without striving to see the probable future, many of our future imaginings will be unconstrained fantasies, and preferences that will collide with reality. Once we resolve to value the probable future at the same level as the possible and the preferable, we can see the world as it should be seen, and produce far better foresight as a result.