The structure of scientific evolution

Science is the construction and testing of systems that bind symbols to sensations according to rules. Material implication is the primary rule, providing the structure of definition, elaboration, delimitation, prediction, explanation, and control. The goal of science is not to secure truth, which is a binary function of accuracy, but rather to increase the information about data communicated by theory. This process is symmetric and thus entails an increase in the information about theory communicated by data. Important components in this communication are the elevation of data to the status of facts, the descent of models under the guidance of theory, and their close alignment through the evolving retroductive process. The information mutual to theory and data may be measured as the reduction in the entropy, or complexity, of the field of data given the model. It may also be measured as the reduction in the entropy of the field of models given the data. This symmetry explains the important status of parsimony (how thoroughly the data exploit what the model can say) alongside accuracy (how thoroughly the model represents what can be said about the data). Mutual information is increased by increasing model accuracy and parsimony, and by enlarging and refining the data field under purview.