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具有词汇结构的复杂agent网络语言竞争模型

  • 王超 ,
  • 毕贵红 ,
  • 张寿明 ,
  • 于群修 ,
  • 王超 ,
  • 毕贵红 ,
  • 张寿明 ,
  • 于群修
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  • 1.昆明理工大学信息a.工程与自动化学院,b.昆明理工大学电力工程学院,昆明 650500;
    2.国家知识产权局专利局专利审查协作天津中心,天津 300280
王超(1989-),男,天津人,硕士,主要研究方向为复杂系统与复杂网络建模与仿真。

收稿日期: 2014-10-12

  修回日期: 2015-07-09

  网络出版日期: 2025-02-25

基金资助

国家自然科学基金(61364022)

Complex Agent Network with Vocabulary Structure Model for Language Competition Model

  • WANG Chao ,
  • BI Guihong ,
  • ZHANG Shouming ,
  • YU Qunxiu ,
  • WANG Chao ,
  • BI Guihong ,
  • ZHANG Shouming ,
  • YU Qunxiu
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  • 1. a.College of Information Engineering and Automation, 650500, China; b. College of Electric Power Engineering, Kunming University of Science and Technology, Yunnan 650500, China;
    2. Tianjin Center for patent review of the Patent Office of the State Intellectual Property Office, Tianjin 300280, China

Received date: 2014-10-12

  Revised date: 2015-07-09

  Online published: 2025-02-25

摘要

利用agent社会圈子网络理论构建语言竞争网络,利用两个F长度的二进制位串表示可能的双语内部结构,在此基础上给出一种新的动态微观语言竞争模型。分析了语言地位、网络结构、语言遗传率和语言学习率对网络中agent所掌握的词汇量、语言类型及熟练度演化的影响。模型反映了不同语言者之间通过交流和竞争实现语言内部结构演化涌现宏观语言态势的机制。仿真结果表明,模型克服了其他模型不能反映系统中语言使用机会和频次对语言竞争的影响。

本文引用格式

王超 , 毕贵红 , 张寿明 , 于群修 , 王超 , 毕贵红 , 张寿明 , 于群修 . 具有词汇结构的复杂agent网络语言竞争模型[J]. 复杂系统与复杂性科学, 2016 , 13(2) : 53 -66 . DOI: 10.13306/j.1672-3813.2016.02.007

Abstract

This paper proposes a new dynamic social network model to study the competition between two languages (language A and B), this model is based on agent modeling method and social circles theory, and this method also introduces a language structure with two F-length bit-strings vocabulary into the agent which is a node of the network. The evolutions of the average number of words of each language and mastery of the language for agents in the network were analyzed by changing the language states, the structure of social network and the probability of language learning and vertical transmission. The proposed model facilitates the study of the mechanisms of macro behavior which emerges from local interactions and language competition between agents and the evolution of internal language structure of speakers. The simulation result indicates that the model introduced here does account for two key factors, they are the chance and frequency of language usage which influence language competition and do not consider well in other models.

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