Info-gap theory is a methodology for supporting model-based decisions under severe uncertainty. An info-gap is a disparity between what is known, and what needs to be known in order to make a comprehensive and reliable decision. An info-gap is resolved when a surprise occurs, or a new fact is uncovered, or when our knowledge and understanding change. We know very little about the substance of an info-gap. For instance, we rarely know what unusual event will delay the completion of a task, or what mechanical properties are displayed by a newly invented material. Even more strongly, we cannot know what is not yet discovered, such as tomorrow’s news, or future scientific theories or technological inventions. The ignorance of these things are info-gaps. An info-gap is not characterized by a probability distribution.

Info-gap theory has been applied to many areas, including engineering analysis and design, biological conservation, economics, project management, medicine, homeland security, and other areas.