Local Utility Elicitation in GAI Models
This work addresses the practical challenge of parameter elicitation in structured utility models for decision-making, representing an incremental improvement over existing methods.
The paper tackles the problem of eliciting parameters in Generalized Additive Independence (GAI) models for multiattribute utility functions by proposing a procedure that uses local utility queries instead of global ones, and demonstrates its application in a large GAI model using a myopic value-of-information approach.
Structured utility models are essential for the effective representation and elicitation of complex multiattribute utility functions. Generalized additive independence (GAI) models provide an attractive structural model of user preferences, offering a balanced tradeoff between simplicity and applicability. While representation and inference with such models is reasonably well understood, elicitation of the parameters of such models has been studied less from a practical perspective. We propose a procedure to elicit GAI model parameters using only "local" utility queries rather than "global" queries over full outcomes. Our local queries take full advantage of GAI structure and provide a sound framework for extending the elicitation procedure to settings where the uncertainty over utility parameters is represented probabilistically. We describe experiments using a myopic value-of-information approach to elicitation in a large GAI model.