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posted 24. July 2004 08:19
Information as a Measure of Variation
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.
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[ 24. July 2004, 08:22: Message edited by: Moderator ]
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