Foresight practitioner Richard Slaughter defines his craft as “the ability to create and maintain a high-quality, coherent, and functional forward view, and to use the insights arising in useful organizational ways.” As an overview, Slaughter’s rendering is fairly comprehensive, but it only hints at some of the critical features of the foresight process – albeit for necessity.
For one, the “forward view” that foresight enables us to create and maintain is, in most cases, actually several forward views, or scenarios, systematically exploring a space of possibilities. Foresight practitioners often stress the need to pluralize the future. At first blush, this sentiment can either confuse or exasperate, depending on the hearer’s temperament and tolerance for ambiguity. What could it possibly mean? Isn’t there only going to be one future?
The call to pluralize the future becomes especially confusing when, as it often happens, people elide over the difference between foresight practitioners and trend forecasters. They assume that, like forecasters, foresight strategists are in the business of telling others what the future will be like, and that foresight is the crystal ball in which we catch a glimpse of tomorrow.
Let’s be clear: We foresight practitioners have no such crystal ball. Nor does anyone else.
Never mind predicting the future of culture, markets, and politics: Accurately predicting anything less trivial than near certainties, like the sun’s rising tomorrow, is gruelingly hard work. Even in a relatively circumscribed domain of inquiry, accurate prediction work improves only through highly original thought, painstaking research and development, piecemeal engineering efforts, and relentless, extended troubleshooting and fine-tuning, sometimes spun out over decades or even generations.
Take weather as an example. Just thirty years ago, hurricane prediction systems would routinely miscalculate hurricane landfalls by as many as 350 miles. Today, the average miss is about 100 miles: a more than threefold improvement in the predictive power of hurricane modeling. It’s undoubtedly impressive, but the amount of effort expended in achieving even this change in predictive power boggles the mind. As statistician Nate Silver relates in his book The Signal and the Noise, wrangling nonlinear, dynamic systems like hurricanes means, for instance, accounting for fluctuating barometric pressures to the fourth decimal place. Meteorologists discovered that rounding to the third decimal would lead to confusing results, in which the same predictive weather model “would somehow forecast clear skies over Colorado in one run and a thunderstorm in the next.”
Computing the complex interactions of social, technological, environmental, economic, and political factors in order to make real, measurable progress in the rates of accurate prediction of world events is an immeasurably harder – perhaps even intractable – task, and one that we’ve barely started to grasp. The current state of the art, if one could call it that, is quite dismal: Philip E. Tetlock’s long term study of political forecasts, reported in his book Expert Political Judgment: How Good Is It? How Can We Know?, found, for instance, that when political experts described an event as being absolutely certain, it failed to happen one fourth of the time. What’s more, expert performance was only marginally better than that of dilettantes in the subject area, and marginally worse than mindless algorithms enacting bare bones models of change. If weighty political experts were the example par excellence of our predictive capabilities in the realm of political events, this would be as if our most sophisticated hurricane modeling systems barely outperformed someone sticking their finger in the air in order to judge the direction of the wind, and were somewhat less accurate at predicting hurricanes than coin flips.