Commun. Comput. Phys.,
Notice: Undefined index: year in /var/www/html/issue/abstract/readabs.php on line 20

Notice: Undefined index: ppage in /var/www/html/issue/abstract/readabs.php on line 21

Notice: Undefined index: issue in /var/www/html/issue/abstract/readabs.php on line 23
Volume 3.


Language Change and Social Networks

Jinyun Ke 1*, Tao Gong 2, William S-Y Wang 2

1 English Language Institute, University of Michigan, Ann Arbor, MI 48104-2028, USA.
2 Language Engineering Laboratory, Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong.

Received 5 August 2007; Accepted (in revised version) 27 August 2007
Available online 11 December 2007

Abstract

Social networks play an important role in determining the dynamics and outcome of language change. Early empirical studies only examine small-scale local social networks, and focus on the relationship between the individual speakers' linguistic behaviors and their characteristics in the network. In contrast, computer models can provide an efficient tool to consider large-scale networks with different structures and discuss the long-term effect of individuals' learning and interaction on language change. This paper presents an agent-based computer model which simulates language change as a process of innovation diffusion, to address the threshold problem of language change. In the model, the population is implemented as a network of agents with age differences and different learning abilities, and the population is changing, with new agents born periodically to replace old ones. Four typical types of networks and their effect on the diffusion dynamics are examined. When the functional bias is sufficiently high, innovations always diffuse to the whole population in a linear manner in \textit{regular} and \textit{small-world }networks, but diffuse quickly in a sharp S-curve in \textit{random} and \textit{scale-free} networks. The success rate of diffusion is higher in regular and small-world networks than in random and scale-free networks. In addition, the model shows that as long as the population contains a small number of statistical learners who can learn and use both linguistic variants statistically according to the impact of these variants in the input, there is a very high probability for linguistic innovations with only small functional advantage to overcome the threshold of diffusion.

AMS subject classifications: 91.D30
PACS: 89.65.-s, 89.75.Hc
Key words: Language change, social network, agent-based modeling.

*Corresponding author.
Email: jyke@umich.edu (J. Ke), gtojty@gmail.com (T. Gong), wsywang@ee.cuhk.edu.hk (W. S. Y. Wang)
 

The Global Science Journal