Zipf's Law of Abbreviation, the idea that more frequent symbols in a code are simpler than less frequent ones, has been shown to hold at the level of words in many languages. We tested whether it holds at the level of individual written characters. Character complexity is similar to word length in that it requires more cognitive and motor effort for producing and processing more complex symbols. We built a dataset of character complexity and frequency measures covering 27 different writing systems. According to our data, Zipf's Law of Abbreviation holds for every writing system in our dataset, the more frequent characters have lower degrees of complexity and vice-versa. This result provides further evidence of optimization mechanisms shaping communication systems.
