Risk Modeling for Policy Making

Supporting policy makers requires tools to aid in decision making in risky situations. Fundamental to this kind of decision making is a need to model the uncertainty associated with a course of action, an alternative’s uncertainty profile. In addition to this we need to be able to model the responsible agents decision function, their attitude with respect to different uncertain risky situations. In the real world both these kinds of information are to complex, ill defined and imprecise to be able to be realistically modeled by conventional techniques. Here we look at new techniques arising from the modern technologies of computational intelligence and soft computing. The use of fuzzy rule based formulations to model decision functions is investigated. We discuss the role of perception based granular probability distributions as a means of modeling the uncertainty profiles of the alternatives. Tools for evaluating rule based decision functions in the face of perception based uncertainty profiles are presented. We suggest a more intuitive and human friendly way of describing uncertainty profiles is in terms of a perception based granular cumulative probability distribution function. We show how these perception based granular cumulative probability distributions can be expressed in terms of a fuzzy rule based model.