Hashimoto T, Ikegami T
Institute of Physics, College of Arts and Sciences, The University of Tokyo, Japan.
Biosystems. 1996;38(1):1-14. doi: 10.1016/0303-2647(95)01563-9.
Evolution of symbolic language and grammar is studied in a network model. Language is expressed by words, i.e. strings of symbols, which are generated by agents with their own symbolic grammar system. Agents communicate with each other by deriving and accepting words via rewriting rule set. They are ranked according to their communicative effectiveness: an agent which can derive less frequent and less acceptable words and accept words in less computational time will have higher scores. They can evolve by mutational processes, which change rewriting rules in their symbolic grammars. Complexity and diversity of words increase in the course of time. The emergence of modules and loop structure enhances the evolution. On the other hand, ensemble structure lead to a net-grammar, restricting individual grammars and their evolution.
在一个网络模型中研究了符号语言和语法的演变。语言由单词来表达，即符号串，这些符号串由具有自身符号语法系统的主体生成。主体通过重写规则集推导和接受单词来相互交流。它们根据交际效率进行排名：一个能够推导频率较低且可接受性较低的单词，并在较少的计算时间内接受单词的主体将获得更高的分数。它们可以通过突变过程进化，这些过程会改变其符号语法中的重写规则。随着时间的推移，单词的复杂性和多样性会增加。模块和循环结构的出现促进了进化。另一方面，整体结构导致一种网络语法，限制了个体语法及其进化。
