Making even more use of uncertainty - probabilities

However, service A can go further and quantify how uncertain the rain is. This is best done by expressing the uncertainty of rain in probabilistic terms. If “don’t know” is equal to 50% [4] then 60% and 80% indicate less uncertainty, 40% and 20 % larger uncertainty. Over a 10-day period the contingency table might, on average, look like this, where the four cases of uncertain forecasts have been grouped according to the degree of uncertainty or certainty:

Table 7

The use of probabilities will allow other end-users, with protection costs different from X’s and Y’s, to benefit from A’s forecast service. They should take protective action if the forecast probability exceeds their cost/loss ratio (P > c/L). Assuming possible losses of €100, someone with a protection cost of €30 should take action when the risk > 30%, someone with costs of €75, when the forecast is of >75% probability (see Figure 80).

Figure 80:  The same figures but with the expected expenses indicated for cases where different end-users take action after receiving probability forecasts. The general performance (thick blue line) is now closer to the performance for perfect forecasts.

X lowers his expenses to €10 and Y to €24.