Selecting the Right Signals

What is it for?
Broadly speaking, you can separate signals into two categories: internal signals for developing forecasts and external signals for illustrating those forecasts.

Internal signals for developing forecasts
If you are looking for signals for your own research, then evaluation is pretty straightforward. As a foresight practitioner, you begin to keep a vague sense of the range of future possibilities in the back of your head. When you encounter signals, they can bring definition to those possibilities, making them seem more or less plausible, and they can also spark entirely new possibilities in your mind. The better the signal, the more it modifies your semi-conscious map of possibilities on the horizon. (And of course, when you're researching for a particular project, it's a good signal if it changes your perception of what is possible vis-a-vis the project's topic.) You can extend this rubric to teammates or other research collaborators, ("does it stretch their thinking the way it stretches yours?").

External signals for illustrating forecasts

If instead, the signal is meant for an audience who is going to encounter it as part of a foresight report, map presentation, or scenario, then it gets a little more complicated. Signals are, of course, a research input, and sometimes the signals you used to develop the forecasts are also great for explaning them. But many times, your internal signals aren’t the best choice for illustrating a forecast. Essentially, once you have a forecast clear in your head or amongst your team, your job is to persuade your audience that your forecast is plausible and worth paying attention to—and you use signals as a tool to do that. So when selecting the signals to use, you should ask the following questions:

• Does it make the forecast more clear? The primary function of a signal in a forecast deliverable is to clarify the forecast for the audience. They function as a real world example that illustrates how something might work in the future. This means selecting the signal that will be the easiest for the audience to grasp, not necessarily the one you, as a forecast practitioner, find most interesting.
• Does it make the forecast more plausible? The secondary function is to convince your audience that your forecast is plausible. This, of course, means understanding your audience and choosing the kind of signal they find most convincing, (for example, some audiences find data points or results of research studies uniquely credible, for others, art projects and anecdotes might be the most compelling). But one general principle that applies to almost all audiences is that a signal is never convincing on its own. You'll need to use drivers (more about drivers here) to make it clear that the signal is worth paying attention to and that it does make your forecast more plausible.
• Does it add anything different? In most forecasts, you'll want to include multiple signals. While some audiences find it helpful and convincing to see several similar signals, it's usually better to select signals that are different from one another in substantial ways. (One thing to keep in mind, is that if you can find too many similar signals, they might not actually be signals at all and are, instead, drivers.) It's important to think about how each signal contributes something different to your forecast. For instance, you can select your signals to make a point about how the forecast might unfold in different regions and for different socioeconomic groups. You can also combine signals to add important nuances or caveats to your forecast.
• Does it tell a good story? One important function of a signal that often goes undiscussed is its storytelling value. Good signals provoke your imagination and leave a lasting impression. While signals often appear as bullet points in a map or report, without a lot of room for detail, in presentations or other more in-depth formats, signals can be explained as a story that makes the forecast more engaging and memorable for your audience. If the people involved in the signal, the way the signal came to be, or the impacts it had are interesting or surprising, then it can be narrativized effectively.

These are some of the principles we use to select signals. What are some of yours? Share your suggestions at [email protected]!

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