Eliciting Utilities by Refining Theories of Monotonicity and Risk

Angelo Restificar, Peter Haddawy, Vu Ha, and John Miyamoto

Interest in such diverse problems as development of user-adaptive software and greater involvement of patients in medical treatment decisions has increased interest in development of automated preference elicitation tools. A design challenge of these tools is to elicit reliable information while not overly fatiguing the interviewee. We address this problem by using domain background knowledge in a flexible manner. In particular, we use knowledge-based artificial neural networks to encode assumptions about a decision maker’s preferences. The network is then trained using answers to standard gamble type questions. We explore the use of a domain theory encoding simple monotonicity assumptions and another additionally encoding assumptions concerning attitude toward risk. We present empirical results using a data set of real patient preferences showing that learning speed and accuracy increase as more domain knowledge is included in the neural net.


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