Clarifying the representation and modeling of uncertainty and risk in design problems

Abstract: Decision problems under conditions of uncertainty and risk are challenging. To begin with uncertainty and risk representations in the literature are typically problem specific and the methodologies that can handle these formulations are computationally demanding.

In the methodology presented here, an attempt as been made to expand the representation and modeling of uncertainty and risk to be applicable to a wide class of decision problems. Separable stochastic approximations of random fields and functional representations of risk are the major tools of this methodology. The approach reduces computational complexity for decision models while remaining useful for decision making.

This methodology has the potential of resolving complex problems such as multicriteria portfolio selection, engineering design problems, etc, beyond the scope of conventional mathematical programming and data analysis.

Work performed under the direction of Dr.James Reneke