Patient centered decision making requires people with chronic illnesses to balance low probability events with ambiguity and uncertainty. A robust-satisficing decision strategy provides an alternative to an optimizing one that would yield a lack of confidence in the ultimate choice. Instead, information-gap decision theory (IGT) provides reliable and adequate although likely sub-optimal results that maximize immunity to uncertainty (robustness.) Additionally IGT shows the opportunity for unexpectedly propitious or windfall possibilities. This approach obviates the lack of confidence that the patient has when unrealistic optimizing strategies are pursued.

IGT is particularly useful for a patient or family making decisions under severe uncertainty and choosing among mutually exclusive options. A patient and family are always uncertain about how the broad range of epidemiologic data applies to them. They are also uncertain about how satisfactory or unsatisfactory the unfamiliar potential outcomes would be. IGT approaches this problem by maximizing the robustness to uncertainty in the decision and satisificng rather than maximizing the quality of the outcome. We illustrate this with an in silico model of the mutually exclusive choices in prostate cancer using a robust satisficng model and Web interface. Finally, we model the ability of analysis of the satisficing strategies that a person pursues to reveal his or her preferences and show that these can be reflected back to improve the decision.