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原文連結
論文資訊
- 類型：已發表論文
- 日期：2010
摘要
Zipf's law is the most common 統計 distribution displaying 縮放律 behavior. Cities, 族群s or firms are just examples of this seemingly universal law. Although many different models have been proposed, no general theoretical explanation has been shown to exist for its universality. Here, we show that Zipf's law is, in fact, an inevitable outcome of a very general class of 隨機 systems. Borrowing concepts from Algorithmic 資訊 Theory, our derivation is based on the properties of the symbolic sequence obtained through successive observations over a system with an ubounded number of possible states. Specifically, we assume that the complexity of the description of the system provided by the sequence of observations is the one expected for a system evolving to a stable state between order and disorder. Th
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