Robust decision first used in the late 1990s. It is used to identify decisions made with a process that includes formal consideration of uncertainty. The self-published book Making Robust Decisions gives a formal definition: A robust decision is the best possible choice, one found by eliminating all the uncertainty possible within available resources, and then choosing, with known and acceptable levels of satisfaction and risk.
The name and methods began with the field of “Robust Design” popularized primarily by Genichi Taguchi. Robust decision making extends the robust design philosophy to general decision making, with uncertainty considered from the beginning: controlling what uncertainty you can and finding the best possible solution that is as insensitive as possible to the remaining uncertainty. Thus, developing robust decisions depends on the ability to manage uncertainty. Formal methods to accomplish this rely on Bayesian inference.