Information Gap Decision Theory: Decisions under Severe Uncertainty

Ben-Haim (mechanical engineering, Technion-Israel Institute of Technology) discusses the use of quantitative models and computational methods used for the formulation of decision algorithms and performance assessment in situations where the decision-making process is open-ended and characterized by a severe lack of information. Rejecting the current models of probability theory, he argues that a new theory (termed ''info-gap'' theory), which arose in the technological sciences, offers a methodology that takes into account the potentials of great success and great failure. The basic decision functions of the theory are the robustness function, which assesses the immunity to failure, and the opportunity function, which assesses the immunity to windfall. These concepts interact with functions of value judgments, gambling and risk-taking, value of information, assimilation of data, and coherent uncertainties to form the basics of info-gap theory.