Humans have long tried to determine the shape of what’s to come. But even the most advanced technology can’t solve the fundamental issues with predictions.
In the last century, technology legitimized the latter approach, as developments in IT provided more powerful tools and systems for forecasting. In the 1940s, the analog computer MONIAC had to use actual tanks and pipes of colored water to model the UK economy. By the 1970s, the Club of Rome could turn to the World3 computer simulation to model the flow of energy through human and natural systems via key variables such as industrialization, environmental loss, and population growth.
This approach resonates in many ways with yet another forecasting method—war-gaming. Beginning in the 20th century, military field exercises and maneuvers were increasingly supplemented, and sometimes replaced, by simulation. Undertaken both by human beings and by computer models such as the RAND Strategy Assessment Center, this strategy is no longer confined to the military, but is now used extensively in politics, commerce, and industry.
More recently, research at MIT has focused on developing algorithms to predict the future based on the past, at least in the extremely short term. By teaching computers what has “usually” happened next in a given situation—will people hug or shake hands when they meet?—researchers are echoing this search for historical patterns. But, as is often a flaw in this approach to predictions, it leaves little room, at least at this stage of technological development, to expect the unexpected.
But if predictions based on past experience have limited capacity to anticipate the unforeseen, extrapolations from techno-scientific innovations have a distressing capacity to be deterministic. Ultimately, neither approach is necessarily more useful than the other, and that’s because they both share the same fatal flaw—the people framing them.of the forecaster, and however sophisticated their tools, the trouble with predictions is their proximity to power.
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