Decoding least effort and scaling in signal frequency distributions

Here, assuming a general communication model where objects map to signals, a power function for the distribution of signal frequencies is derived. The model relies on the satisfaction of the receiver (hearer) communicative needs when the entropy of the number of objects per signal is maximized. Evidence of power distributions in a linguistic context (some of them with exponents clearly different from the typical betaapproximate to2 of Zipt's law) is reviewed and expanded. We support the view that Zipt's law reflects some sort of optimization but following a novel realistic approach where signals (e.g. words) are used according to the objects (e.g. meanings) they are linked to. Our results strongly suggest that many systems in nature use non-trivial strategies for easing the interpretation of a signal. Interestingly, constraining just the number of interpretations of signals does not lead to scaling. (C) 2004 Elsevier B.V. All rights reserved.