Despite all the information generated by risk analysis science, many judgments and decisions are still made intuitively rather than analytically. Four decision-making strategies are described based on the complexity of the decision task and of the decision context. The central role of evidence in risk management decision making is reviewed. This leads to an important strategic question: how much evidence is enough? The best time to decide is considered in this context. Several classifications for uncertainty are considered and strategies for decision making under deep uncertainty, including resistance, resilience, adaptive and static robustness, the precautionary principle, and others are discussed. Risk managers need a practical approach for making decisions under uncertainty and a four-step approach is offered. Practical means for implementing this approach are presented, including: changing the decision meeting, questioning the numbers, powering down decision making, and socializing good errors from good risk management. The need for new risk metrics is discussed and followed by some traditional decision analysis techniques including value of information strategies.