Decisions, decisions, decisions in an uncertain environment

TL;DR

This paper emphasizes the importance of incorporating uncertainty quantification into decision-making processes, especially in environmental policy, and advocates for advancing statistical decision theory to be more practical and relevant.

Contribution

It highlights the gap in current decision-making where uncertainty is often overlooked and calls for a more proactive role of statistical science in decision applications.

Findings

Uncertainty is often neglected at the decision stage.

Advances in data and information have not fully translated into decision-making.

Statistical science should evolve to better support practical decision processes.

Abstract

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.