  Decision-makers abhor uncertainty, and it is certainly true that the less
there is of it the better. However, recognizing that uncertainty is part of the
equation, particularly for deciding on environmental policy, is a prerequisite
for making wise decisions. Even making no decision is a decision that has
consequences, and using the presence of uncertainty as the reason for failing
to act is a poor excuse. Statistical science is the science of uncertainty, and
it should play a critical role in the decision-making process. This opinion
piece focuses on the summit of the knowledge pyramid that starts from data and
rises in steps from data to information, from information to knowledge, and
finally from knowledge to decisions. Enormous advances have been made in the
last 100 years ascending the pyramid, with deviations that have followed
different routes. There has generally been a healthy supply of uncertainty
quantification along the way but, in a rush to the top, where the decisions are
made, uncertainty is often left behind. In my opinion, statistical science
needs to be much more pro-active in evolving classical decision theory into a
relevant and practical area of decision applications. This article follows
several threads, building on the decision-theoretic foundations of loss
functions and Bayesian uncertainty.
