System Modeling and Information Semantics

Floridi ́s Theory of Strongly Semantic Information defines information as consisting of data and truth in contrast to the standard definition prevailing in empirical sciences in which information is defined as meaningful data. I argue that meaningful data does not necessarily need to be true to constitute information. Partially true information or even completely false information can lead to an outcome adequate and relevant for inquiry. Instead of insisting on the truth of an empirical model, the focus is on basic criteria such as the validity of the model and its appropriateness within a certain well-defined context, as the meaning of the information content of the model is strongly contextual. Even though empirical models could in general only be ‘adequate’ and not ‘true’ they may produce results and data from which relevant conclusions could be drawn. If truthlikeness admits of degrees, then the history of inquiry is one of steady progress towards the truth. In that sense models can generate information for improving our knowledge about the empirical world