Testing an agent-based model of language choice on sociolinguistic survey data

The paper outlines an agent-based model for language choice in multilingual communities and tests its performance on samples of data drawn from a large-scale sociolinguistic survey carried out in Estonia. While previous research in the field of language competitionhas focused ondiachronic applications, utilizing rather abstractmodels of uniformspeakers,weaim tomodel synchronic language competition amongmore realistic, data-based agents. We hypothesized that a reasonably parametrized simulation of interactions between agents endowed with interaction principles grounded in sociolinguistic researchwould give rise to a network structure resembling real-world social networks, and that the distribution of languages used in the model would resemble their actual usage distribution. The simulation was reasonably successful in replicating the real-world scenarios, while further analysis revealed that themodel parameters differ in importance between samples.We conclude that such variation should be considered in parametrizing future language choice and competition models.

[1]  Marko Dragojevic,et al.  Communication Accommodation Theory , 2015 .

[2]  Maxi San Miguel,et al.  Modeling two-language competition dynamics , 2012, Adv. Complex Syst..

[3]  R. Clément,et al.  Willingness to Communicate in a Second Language , 2003 .

[4]  James W. Minett,et al.  Modelling endangered languages: The effects of bilingualism and social structure , 2008 .

[5]  Jeanine Treffers-Daller,et al.  Dynamics of language contact: English and immigrant languages , 2005 .

[6]  D. W. Scott,et al.  Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .

[7]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[8]  Neus Isern,et al.  Language extinction and linguistic fronts , 2014, Journal of The Royal Society Interface.

[9]  W. Bruce Croft,et al.  Modeling language change: An evaluation of Trudgill's theory of the emergence of New Zealand English , 2009, Language Variation and Change.

[10]  A. Dekker,et al.  Realistic Social Networks for Simulation using Network Rewiring , 2007 .

[11]  Daniel J. Brass,et al.  Network Analysis in the Social Sciences , 2009, Science.

[12]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[13]  Matthieu Latapy,et al.  Computing Communities in Large Networks Using Random Walks , 2004, J. Graph Algorithms Appl..

[14]  K. Kaski,et al.  A Model For Social Networks , 2006, physics/0601114.

[15]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[17]  Achim Zeileis,et al.  Bias in random forest variable importance measures: Illustrations, sources and a solution , 2007, BMC Bioinformatics.

[18]  Achim Zeileis,et al.  Conditional variable importance for random forests , 2008, BMC Bioinformatics.

[19]  Teemu Leppänen,et al.  Modeling language competition , 2004 .

[20]  Maxi San Miguel,et al.  Agent-based models of language competition , 2013 .

[21]  Charalampos E. Tsourakakis.  Fast Counting of Triangles in Large Real Networks without Counting: Algorithms and Laws , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[22]  Tobias H. Stark,et al.  Understanding the Selection Bias , 2015 .

[23]  A. Kandler,et al.  Language shift, bilingualism and the future of Britain's Celtic languages , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[24]  Mikael Parkvall,et al.  Modeling the Evolution of Creoles , 2015 .

[25]  Tao Gong,et al.  Principles of parametric estimation in modeling language competition , 2013, Proceedings of the National Academy of Sciences.

[26]  Tao Gong,et al.  A Computational Framework to Simulate the Coevolution of Language and Social Structure , 2004 .

[27]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  P. Bühlmann,et al.  Survival ensembles. , 2006, Biostatistics.

[29]  D. B. Hess,et al.  Preferences Toward Neighbor Ethnicity and Affluence: Evidence from an Inherited Dual Ethnic Context in Post-Soviet Tartu, Estonia , 2015 .

[30]  M. Tomassini,et al.  Cooperation and community structure in social networks , 2008 .

[31]  H. Giles.  Towards a theory of language in ethnic group relations , 1977 .

[32]  S. Strogatz,et al.  Linguistics: Modelling the dynamics of language death , 2003, Nature.