Bayesian Interactive Decision Support for Multi-Attribute Problems with Even Swaps

Even swaps is a method for solving de-terministic multi-attribute decision problems where the decision maker iteratively simpli-fies the problem until the optimal alterna-tive is revealed (Hammond et al. 1998, 1999). We present a new practical decision support system that takes a Bayesian approach to guiding the even swaps process, where the system makes queries based on its beliefs about the decision maker’s preferences and updates them as the interactive process un-folds. Through experiments, we show that it is possible to learn enough about the decision maker’s preferences to measurably reduce the cognitive burden, i.e. the number and com-plexity of queries posed by the system.