Chapter 11. Evo Devo Foresight: Unpredictable and Predictable Futures

1. Evolutionary Factors

B. Imaginations, Combinatorials, Emergences, & Divergences

Our imaginations offer us another powerful road to evolutionary foresight. Brainstorming is the technique most commonly associated with creative imagining, a process of initially “high quantity, no quality evaluation” idea production that can open us up to seeing outcomes that we didn’t realize were possible. Using design thinking, reading science fiction and creative literature, using CLA and other methods can greatly expand our ability to imagine outcomes. Methods like futures wheels, which explore possible consequences and outcomes via causal chains, branching out from the central trend, event, or issue being explored, are another very powerful way to harness our imaginations to map a possibility space.

Another well-used evolutionary foresight approach is the exploration of combinatorials of possibilities. This can be done at a fine level of granularity, with methods like cross-impact analysis, a way of exploring outcomes by putting causal factors, issues, or other entities in an n-by-n matrix, or a low dimensional set of matrices, and exploring all the ideas or outcomes suggested by combining each of those entities. Many locations on the matrix can be silly, causing us to consider combination of words and ideas that don’t make sense. But they can also be insightful, showing us a few combinations and possible implications, that we hadn’t considered.

We can also explore outcome possibilities at a coarse level of granularity with methods like scenario analysis, which require us to determine particularly important and/or uncertain outcomes, causes or driving forces, on a very small number of dimensions, and build stories about the futures that would exist if those particular combinations occurred.

Emergences, or the looking for emergent new complex adaptive systems that are more than the sum of their combined parts, occuring via the collective interaction of simpler rules and systems, is another powerful way to explore the possibility space. A few emergences will be developmental, but of course the vast majority will be evolutionary, useful in particular times and places, but not broadly optimal, versus other kinds of emergences. Both John Holland’s Emergence (1999) and Steven Johnson’s Emergence (2002) offer good popular introductions to this universal process. Miller and Page’s Complex Adaptive Systems (2007) is a popular technical work. Thinking carefully about the conditions necessary for emergence of new complex adaptive systems in physics, chemistry, and biology, can help us greatly to look for those conditions in society and technology.

Another set of powerful, engineering based tools of possibility foresight involve the structured exploration of divergences from our current condition. Futures wheels can do this at a very basic level, but there are many more powerful formal methods like TRIZ, morphological analysis, and degrees of freedom analysis that can be used to explore the dimensionality of complex systems. One particularly promising approach in the exploration of divergences is to look especially hard for those newly emerging systems, platforms, or tools that will greatly improve the thinking or behavioral options available to people. Emergences like electricity, cars, computers, phones, and software often create powerful divergences, greatly expanding the possibility space.

Drones, which emerged on top of the consumer smartphone industry, and took advantage of existing trends in fixed wing radio-control (RC) toy planes, which were moving from gas to electric power, were launched by a few clever explorations of the possibility space by small groups of lead users in university and hobbyist communities. As Tom Standage explains in The Economist, perhaps the key insight occurred in 2005, with the conversion of RC electric helicopter control from its complex and hard-to-fly setup, the same used in traditional helicopters, to a much simpler and stabler system with three or more vertical-axis rotors, each of which can be spun faster or slower to steer the drone. Other enabling innovations in open source autopilot software, GPS, accelerometers, cameras, and wi-fi chips all followed this critical innovation, again enabled by smartphones and open hardware. In 2017, just twelve years later, the first drone delivery networks for commercial goods was launched in China, with teleoperated drones, and in 2018, the first air taxis for delivering people are scheduled to begin in Dubai. One wonders how much earlier we could have discovered drones, if a crowd of engineers, using a foresight methods like TRIZ, had been tasked with the problem of simplifying existing RC electric helicopters, to give the world a simpler and better method of precise VTOL flight for small aircraft, an obviously very empowering advance. We’ve had good lithium ion batteries (though they were far less dense) and small electric engines since 1991. I’d bet that drones could have arrived five years earlier, though probably not ten. Batteries were just too low yield for useful drone flight before 2000.

With our individually limited thinking ability, we are often quite poor at thinking through what will happen next at divergence points. Any new tool has thousands of potential applications, and once it arrives, we often see only a few of them. Working in large collectives we can overcome many of our individual thinking limitations. It is particularly helpful, when some new freedom like Twitter emerges, to look for the “killer app” for that tool, the most developmentally dominant users and contexts. We have to mentally consider many possible use cases before it becomes obvious that, among other things, Twitter will be excellent for celebrities, and for quick updates among masses of protestors using smartphones, as in the Arab Spring (2010-present).

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