But the indeterminate future is somehow one in which probability and statistics are the dominant modality for making sense of the world. Bell curves and random walks define what the future is going to look like. The standard pedagogical argument is that high schools should get rid of calculus and replace it with statistics, which is really important and actually useful. There has been a powerful shift toward the idea that statistical ways of thinking are going to drive the future.
We know from chaos theory that even if you had a perfect model of the world, you'd need infinite precision in order to predict future events. With sociopolitical or economic phenomena, we don't have anything like that.
I have been doing technology foresight for a number of years now on the level of scenario design, primarily. I want to become more rigorous with research methodology and statistical methods. I want to shift from creating clever SF scenarios to being a professional forecaster able to make rigorous predictions.
If you have to forecast, forecast often.
You don't want to influence the same system you are trying to forecast.
A forecasting game is a kind of simulation, a kind of scenario, a kind of teleconference, a kind of artifact from the future - and more - that enlists the participants as 'first-person forecasters.'
This is actually a very important principle that science is learning about large systems like evolution and that futurists are learning about anticipating human society: just because a future scenario is plausible doesn't mean we can get there from here.
If that enabled us to predict the succeeding situation with the same approximation, that is all we require, and we should say that the phenomenon had been predicted, that it is governed by the laws.
If each of your time steps is one week long, you are not modeling the stock price terribly well over a one-week time period, because you are saying that there are only two possible outcomes.
Forecasts vary in horizon, from a few seconds up to a few days in financial markets, compared to from one to several months for macro variables. We have to provide uncertainty intervals around the central forecasts to indicate the extent to which we are unclear about the future.
Whenever I see a forecast written out to two decimal places, I cannot help but wonder if there is a misunderstanding of the limitations of the data, and an illusion of precision.
In order to predict effectively, we need to use science. And the reason that we need to use science is because then we can reproduce what we're doing; it's not just wisdom or guesswork. And if we can predict, then we can engineer the future.
