The answer is to apply Bayesian methods of inference. In our case, the forecaster casts the illusion of objectivity away and decides that the best way to combine expert knowledge with historical data is to employ Bayesian forecasting techniques. These methods are consistent with the principles discussed in this paper, and provide optimal ways to cope with uncertainty. This is the final principle we wish to discuss. To answer, we start by making clear the difference among uncertainty, risk, and error.
Uncertainty is pervasive.
It is simply the unknown, and largely unknowable, future. Error is the difference between what we forecast and what actually happens. Risk is what is at stake, what is lost if our forecast is wrong. Since we cannot eliminate uncertainty, we manage it by examining the probability distribution on the outcomes and doing a sensitivity analysis on the model's parameters. We cannot eliminate error, as it arises so easily from unforeseeable factors, deficiencies in the data used in the forecast, the making of faulty assumptions, poor judgement, and the like.