Precautionary decision making: Analysis and results

We address how society can manage potentially severe or irreversible outcomes when different combinations of variability, uncertainty and limited causal knowledge characterise them. We outline a decision-analytic solution focused on risky decisions that accounts for prior scientific beliefs and can be updated as subsequent information becomes available. As a practical and established approach to causal reasoning and decision making under risk, this (Bayesian) method helps decision makers, because it formally accounts for probabilistic outcomes and new information. The rational choice of action from the set of alternative actions requires accounting for costs, benefits, and changes in the probabilities of adverse outcomes associated with each action, as well as for the appropriate measures of uncertainty that characterise those outcomes. Decisions under the precautionary principle must account for the contingent nature of scientific information, linking to the principle of expected value of information (VOI), to show the relevance of new information