Abstract
ABSTRACTThis study posits a scenario in which three famous information theorists meet each other in a bar and compare notes. The three include the celebrated information theorist Claude Shannon, the eighteenth century English statistician Thomas Bayes, and the Harvard statistical linguist George Zipf. Each promoted a foundational equation concerning human communication and the updating of beliefs based on new information. They discover that with some modest mathematical transformations they can demonstrate that each of the equations, although based on entirely distinct phenomena in physics, statistics, and linguistics, has the same basic structural form. The core of the analysis explores how such distinct phenomena share similar nonlinear structural properties, i.e., non-Gaussian distributions and why an understanding of these properties is important for communication research and the analysis of advanced information systems.
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