Decision theory is an analytic and systematic approach to studying decision making. Six steps are usually involved in making decisions in three environments: decision making under certainty, uncertainty, and risk. In decision making under uncertainty, decision tables are constructed to compute criteria such as maximax, maximin, criterion of realism, equally likely, and minimax regret. Methods such as determining expected monetary value (EMV), expected value of perfect information (EVPI), expected opportunity loss (EOL), and sensitivity analysis are used in decision making under risk.

Decision trees are another option, particularly for larger decision problems, when one decision must be made before other decisions can be made. For example, a decision to take a sample or to perform market research is made before we decide to construct a large plant, a small one, or no plant. In this case, we can also compute the expected value of sample information (EVSI) to determine the value of the market research. The efficiency of sample information compares the EVSI to the EVPI. Bayesian analysis can be used to revise or update probability values using both the prior probabilities and other probabilities related to the accuracy of the information source.