We have recently proposed that combining inductive models into decision-making supernetworks is one method for emulating more sophisticated forms of deduction (Fig. ). This idea is based on the hypothesis that deductive reasoning emerges from an interaction between inductively trained models (Fig. ). The basis for this hypothesis is that deductive reasoning is associated with the use of concepts that are themselves learnable by induction. For example, conservation of energy is a concept that can be learned inductively from a sufficient number of examples with clear accounting of energy partitioning before and after a reaction, collision, etc. When looking at a new phenomenon, the conservation of energy may then act as a deductive constraint to predict, say, a chemical potential, or radiative loss in a new problem.