Though we have access to a wealth of information, the main issue is always how to process the available information. How to make sense of all we observe and know. Just like the English alphabet: we know there are 26 letters but unless we put these letters together in a meaningful way, they convey no information. There are infinitely many ways of putting these letters together. Only a small number of those make sense. Only some of those convey exactly what (...) we wish to convey though the message may be interpreted differently by different individuals. That same issue comes up with information: how can we process the information we have? How can we infer and reason under conditions of incomplete observed information? In his seminal book on the philosophy of information, Floridi (2011a) raises a number of open questions. I discuss here one of these questions. That question is how to process information. To do so, I take the more realistic view that information is always limited, incomplete and possibly noisy. I define types of information, relate it to Floridi’s definitions and discuss a basic formulation for processing information under a unified framework. I relate it to some of the basic concepts discussed in the book. (shrink)

Info-metrics is a framework for rational inference based on insufficient information. The complete info-metric framework, accompanied with many interdisciplinary examples and case studies, as well as graphical representations of the theory appear in the new book “Foundations of Info-Metrics: Modeling, Inference and Imperfect Information,” Oxford University Press, 2018. In this commentary, I describe that framework in general terms, demonstrate some of the ideas via simple examples, and provide arguments for using it to transform information into useful knowledge.