Chapter 1 - Overview

Abstract

This chapter discusses that the freedom to decide is an opportunity to err; however, every opportunity is also a possibility for success. A decision, in theory must reflect the range of human propensities plus characteristics, even if the decision agent is a group of individuals or an institution or an automated algorithm serving a human need. The central tenet of the info-gap decision theory is that decisions under severe uncertainty must not demand for more information or at least not much more. Classical statistical decision theory, or Von Neumann–Morgenstern expected utility theory, remain bulwarks for a plethora of decision problems, especially those endowed with fairly well understood and highly structured uncertainties. Uncertainty is the complement of knowledge and info-gap models of uncertainty are formulated minimalistically with respect to the agent's information. An information-gap model of uncertainty is a non-probabilistic quantification of uncertainty. Info-gap models entail no measure functions—neither probability densities nor fuzzy membership functions. In formulating an info-gap model, prior information about the uncertain phenomena is invested in determining the structure of a family of nested sets of events.