Killeen Peter R
Arizona State University.
Behav Anal. 2013 Fall;36(2):325-344. doi: 10.1007/BF03392318.
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.
科学是根据规则构建和测试将符号与感觉联系起来的系统。实质蕴涵是首要规则，它提供了定义、阐述、界定、预测、解释和控制的结构。科学的目标不是确保真理（真理是准确性的二元函数），而是增加理论所传达的数据信息。这个过程是对称的，因此也需要增加数据所传达的理论信息。这种交流中的重要组成部分包括将数据提升到事实的地位、在理论指导下模型的推导以及通过不断发展的溯因过程使它们紧密对齐。理论和数据共有的信息可以通过给定模型时数据领域的熵或复杂性的降低来衡量。也可以通过给定数据时模型领域的熵的降低来衡量。这种对称性解释了简约性（数据对模型所能表达内容的利用程度）与准确性（模型对数据所能表达内容的表征程度）同样重要的地位。通过提高模型的准确性和简约性，以及扩大和完善所涉及的数据领域，可以增加互信息。
