by William A. Dembski

Abstract— Within information theory, information typically measures the reduction of uncertainty that results from the knowledge that an event has occurred. But what if the item of knowledge learned is not the occurrence of an event but, rather, the change in probability distribution associated with an ensemble of events? This paper takes the usual account of information, which focuses on events, and generalizes it to probability distributions/ measures. In so doing, it facilitates the assignment of “generalized bits” to arbitrary state transitions of physical systems. In particular, it provides a theoretical framework for characterizing the informational continuity of evolving systems and for rigorously assessing the degree to which such systems exhibit, or fail to exhibit, continuous change.

The full paper is available below:
Information as a Measure of Variation

Back to PCID Volume 4.2, November 2005