We propose a new variable selection approach from a fully Bayesian decision theory viewpoint. The method draws an explicit distinction between actions and inferences, effectively dealing with the trade-off associated with the competing goals of predictive generalization and interpretability. By decoupling posterior learning from model reporting, our approach creates a flexible framework where continuous shrinkage priors can be used but ``sparse solutions'' can be obtained. The method generalizes straightforwardly to the GLM setting.