A measure of efficiency for influence diagram models with continuous decision variables is presented in order to evaluate whether the additional computational complexity required by a more accurate model is justified. The efficiency measure is a multi-objective utility function that considers both the accuracy and complexity of the ID model. Accuracy is determined as the mean squared error between influence diagram decision rules and an analytical solution. Complexity is assessed by tracking the run time required to obtain the solution. The resulting efficiency score considers the preferences of an individual decision maker for accuracy and complexity. Three influence diagram models are compared using the efficiency measurement, and an iterative solution procedure is introduced to improve model performance.