Representation of preferences in decision-support systems

The recommendations of computer-based decision-support systems depend on the preferences of an expert on which the model is based. Often, these preferences are represented only implicitly, rather than explicitly, in the system. Decision-theoretic preference models that explicitly represent the preferences of the decision maker provide numerous advantages for decision-support systems. In this paper, we describe these advantages. The creation and refinement of decision-theoretic preference models, however, remains a difficult task. We describe an accurate and efficient method for determining the preferences of domain experts and for refining the model that captures those preferences. In this preference-assessment method, we simulate decisions common in the expert's area. We then infer the preferences of the expert from the choices that she makes on the simulated decisions, and use the preference information to refine the model automatically.