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. Therefore there is no point in seeking to minimize error, as we know we will be wrong. Rather, the best we can do is minimize risk. That is, we wish to make the forecast that allows the decision maker to make the least risky decision in terms of what he might lose due to the forecasting error.