Eliciting knowledge for analytical representation

One large class of intelligent systems attempts to represent expert knowledge in terms of an analytical language. If experts can express themselves in terms of that language, then its analytical power allows sophisticated manipulation of their knowledge, perhaps leading to inferences that they could not have made reliably without the help of such a cognitive aid. In addition, these languages allow pooling the knowledge of diverse experts, each providing inputs to different components of the model in a way that is subject to independent review and revision. Examples of such languages include those of decision analysis, forecasting, cost-benefit analysis, and probabilistic risk analysis. Although they are applied to very different substantive problems (e.g. defining terms, estimating quantitative parameters), the analytical representation languages used by such systems require a common set of interdependent judgmental skills. As a result, one can draw on a common set of research results regarding the cognitive processes involved in using these systems. This perspective is illustrated in the context of a particular analytical representation language, that used in risk analysis