Information and data emerging from complex systems are converted, via models and theories, to knowledge about these systems. But can we evaluate the potential value of these data and information, and its interrelationship with the modeling and inferential approaches used to transform the information into knowledge? In this working group we will study these issues within the context of information theory and information-theoretic inference.
Overall, this Working Group deals with a few interdependent questions – all related to our understanding of the information and data in complex systems and the potential value of information. The first question is whether information theory can be used for developing new tools for evaluating the full potential, and potential value, of datasets and the information stored in these data? The second basic question is how to extend (if possible) information theory to account for the meaning of the information embedded in the data? A fundamental issue that arises in both cases is to do with the relationship between the potential value measures and the inferential procedure used to transform data/information to knowledge. The third question is then, should the value be independent of the inferential approach used? The fourth question is how can we measure the value (or potential value) of a model? We hypothesize that it is conditional on the information needed for constructing and developing that model, but it is an open question that demands more thinking.
Though value is always a relative concept, interest in the philosophy, including meaning and value of information as well as other aspects in the philosophy of information, goes back half a century but has rapidly increased recently with the availability of more complex data, as well as many new directions of research into the meaning, measures and quantification of incomplete, large, blurry and complex data. Theoretical advances in these directions will have a substantial impact on a wide range of real-world applications. Methodological and technical advances will assist policy and decision makers and will have a direct impact on private and public agencies that produce data for public and private use and research. For scientists the formulation of such a (relative) potential value will provide an additional tool to evaluate the information used – and needed – in modeling and inference.