Successive improvement of Bayesian solutions

The decision making process under uncertainty is studied with the assumption additional data can be obtained through successive experiments. An algorithm is given for optimal choice of experiments and decision making on the knowledge of their results so as to minimize the mean total costs of experiments and Bayasian losses. The highest manageable computing complexity of the algorithm is estimated.