In applied probability theory, the Simon model is a class of stochastic models that results in a power-law distribution function. It was proposed by Herbert A. Simon[1] to account for the wide range of empirical distributions following a power-law. It models the dynamics of a system of elements with associated counters (e.g., words and their frequencies in texts, or nodes in a network and their connectivity ). In this model the dynamics of the system is based on constant growth via addition of new elements (new instances of words) as well as incrementing the counters (new occurrences of a word) at a rate proportional to their current values.
- ^ Simon, Herbert A. (1955). "On a Class of Skew Distribution Functions". Biometrika. 42 (3–4). Oxford University Press (OUP): 425–440. doi:10.1093/biomet/42.3-4.425. ISSN 0006-3444.