Uncertainty

Abstract In a formal sense, the theory of optimization under uncertainty does not require any new mathematical theory as such. The choice variables lead to random outcomes with objective or subjective probability distributions. The objective functions are appropriate probability-weighted averages (expected values). The choices are also subject to some constraints. The general theory developed in the first eight chapters continues to apply. In fact the special structure of problems of choice under uncertainty leads to specific results that were not available in the general mathematics of constrained optimization.