Optimal decision-oriented Bayesian design of experiments

Traditional ‘Design of Experiment’ (DOE) approaches focus on minimization of parameter error variance. In this work, we propose a new “decision-oriented” DOE approach that takes into account how the generated data, and subsequently, the model developed based on them will be used in decision making. By doing so, the parameter variances get distributed in a manner such that its adverse impact on the targeted decision making is minimal. Our results show that the new decision-oriented DPE approach significantly outperforms the standard D-optimal design approach. The new design method should be a valuable tool when experiments are conducted for the purpose of making R&D decisions.