Knowing probable futures is important for planning. It may be impossible to predict them all the time, but sometimes forecasts are surprisingly good. Hence, chances are we can improve – and make accurate predictions more often.
A logical way to forecast (but not the only one) is to look at the drivers – things or forces causing change – and their possible interplay: what direction does each of them push the situation to, what relative power do they have, what could be their simplest combined effect, and what could be a more acceptable, more sustainable settlement?
This driver-based perspective likens scenarios for wars, markets, games, and other competitive environments: the strongest wins when antagonist sides stubbornly compete with the same resources, but other options are more likely when sides use diverse combinations of resources or when they are more flexible about their priorities.
The approach I propose below is suitable for many fields, because the main options can usually be reduced to a few competing extremes, and, accordingly, their supporters can be viewed as camps.
Typical drivers are people vs ‘big things’
Speaking broadly, any reason for change can be called a driver: forces of nature, viruses, massive and long historical processes, actions of a single human being, and so on.
To simplify the enormous mosaic of possible drivers, we can divide them into two groups by free will: free-will drivers – the ones based on (presumably) the free will of certain animate agents, and no-will drivers – based, respectively, on the phenomena that (presumably) do not have free will.
Animate agents can be people, animals, plants, viruses, bacteria, etc. Hypothetically, there may also exist extraterrestrials, aliens, as well as mythological entities like spirits, gods, ghosts, etc. Science fiction also offers ideas about possible future artificial agents who seem to have free will: robots, general superintelligence, swarm brains, etc.
The very question if anyone has a free will, including people, has been debated for centuries. I do not aim to explore it here, so, for simplicity, I will use a widespread assumption that most of the bigger animate agents have it: people and at least some animals. (Other agents, consequently, do not have it – hence, their activities should be classified as no-will drivers.)
People are obviously the most powerful free-will agents on the Earth. Collectively, they have been stronger than other competing species or adversary circumstances, and they are shaping the planet to their likes nowadays.
Inanimate agents are everything else: material and immaterial resources, for example, tools, roads, foods, institutions, robotic assistants, languages, factories, etc. They do not have free will (presumably), they may not even be able to act themselves, but they influence other agents. This is why they are sometimes called co-agents.
In the short term, the will of (ordinary) people usually plays less important role than conditions and limits represented by ‘massive’ inanimate agents: laws and regulations, distribution of certain material resources, level of technological development, etc.
However, people usually lead the change in the long term, because they can be more active (in distinction to inanimate agents) and inventive.
Speaking figuratively, the most powerful drivers in the short term are typically no-will drivers – ‘big things’, but the role of free-will drivers increases in the long term. There, in the longer term, human ‘victories’ over today’s insurmountable forces are increasingly likely.
But how can we anticipate them?
Human futures follow human desires
Assuming an interplay of certain drivers defines the reality, we can create and update our forecasts based on what is happening with these drivers now.
Assuming the free-will drivers are playing the key role in the long-term, we can make most of long-term forecasts about human futures efficiently by exploring only… people. At least, until other agents become more powerful.
More precisely, to predict future changes, we need to know how two things change: 1) human desires, and 2) their capabilities to realize these desires. If I use an analogy with a car, we need to know the direction it goes in and maximum speed.
Continuing this analogy, we can try to predict probable futures by observing the traffic: what cars go in what directions.
Observing the “traffic” requires some skills like (i) finding future-relevant information and (ii) interpreting it. If you are interested in improving them, I recommend checking, respectively, (i) the topic of horizon scanning, especially the ideas expressed by Molitor (2003), (ii) my article about how to interpret future-relevant information – it will help you collect the necessary future signs and analyse them effectively.
Observe changes in desires and capabilities
We need to answer questions like:
1) Are someone’s desires changing?
If so, how are they changing? How fast and widespread is change? How persistent do the new desires seem to be? What desires are likely to succeed the current ones? If the new desires get realized, what would it imply for our forecast topic – an issue or field, whose futures we aim to predict?
Obviously, we cannot know confidently what someone wants. However, we can get better at knowing it if we practice observing and logical reasoning. It is realistic to figure out someone’s actual intentions, if we know their context and actions.
Desires are a comfortable predictor, because their types are limited to just a few (see an overview of motivation studies in my Master’s thesis). I mean ‘types’ of desires, not ways to satisfy them, which are much more numerous.
Furthermore, desires can follow certain patterns, and it simplifies forecasting even more. Here is a well-known pattern, which I extended in my thesis (for details, please check it here): 1) survival needs – people tend to prioritize them first, 2) then developmental needs, 3) then self-limitation, moderation, keeping balance. If a crisis hits, the highly satisfied individual may return back to the first stage (survival needs).
2) Are someone’s capabilities changing?
Has someone’s access to resources been improving or deteriorating? Is somebody gaining access to absolutely new resources, e.g., through discovery, invention, development? Is someone getting control over resources, lack of which may influence other actors? (For example, water, air, food, mineral resources, land, technologies, information…)
Play as if you were they
Now, based on the answers to the questions above, we can try to draw conclusions about probable futures and their impact on the forecast topic: if certain agents have been changing their desires and/or capabilities, what scenarios are getting more probable?
Common sense and logical thinking will help you. You can imagine being in the shoes of the interested agents. What would you do? What makes sense doing? What would satisfy all the stakeholders, taking into account their expected desires and capabilities?
For example, if we are interested in climate change, we can hypothetically divide the world into two large camps: climate defenders and climate criminals. Here are example combinations of drivers and corresponding probable futures:
Improved access to resources for climate criminals + they do not care about climate at all = faster global warming, bad for all
Improved access to resources for climate criminals + they want to save themselves but not others = slower global warming, yet economic and other difficulties for climate defenders
Improved access to resources for climate defenders + they care about climate = slower global warming, possible difficulties for climate criminals
Similar to strategy games or business games, one can create several future images and check which final state seems to be most reasonable for the expected future desires + capabilities.
As time goes, desires tend to outweigh the initial distribution of capabilities
The longer the forecast period, the more weight desires and motivation have, because free-will agents tend to try more ways to achieve their goals. Respectively, the importance of the initial distribution of capabilities decreases with time.
If time horizon is sufficiently long, then we can ignore the initial distribution of capabilities. What matters then is (i) popularity of a desire: how many people will be interested in the corresponding future image, and (ii) intensity of desiring: how strongly people will prioritize it.
We can assume the strength of their priorities from the pattern mentioned above: survival needs tend to be prioritized strongest.
Thus, the problem of finding the most probable long-term futures is simplified to finding the most probable desires.
If change directions are conflicting, evaluate them also quantitatively
If changes in desires and capabilities suggest moves in the opposite directions, how can we evaluate their probable resulting impact? For example, if one antagonist camp becomes more radical, more intense in their desires, but another camp gets access to more resources.
A part of the answer is time horizon, as I already mentioned above. If the horizon is short, then ‘big things’ (access to more resources) is more likely to win, but the opposite is more likely if the horizon is long.
If the situation is very uncertain, I would propose giving equal weights to both types of changes and evaluating their volume numerically, e.g., as percentage of potential maximum. How big and lasting is change in the access to resources – and how big and (probably) lasting is change in desires?
I propose assigning equal weights to capabilities and desires, because either can play a decisive role. There are examples from the history when determination, strong will won over colossal supply of resources, but it’s also true that there are limits to what can be achieved when resources disparity is huge.
Then some formulas could be proposed to arrive at a single power metric for conflicting changes, and indicative conclusions could be drawn.
Finally, as changes keep happening, we need to update our forecasts accordingly.
Molitor, G. (2003) Molitor forecasting model: key dimensions for plotting the "patterns of change". Journal of Futures Studies. Vol. 8(1), 61-72.