Revisiting transmission and diffusion: An agent-based model of vowel chain shifts across large communities

Abstract

In this study, we present the first agent-based simulation of vowel chain shifts across large communities, providing a parsimonious reinterpretation of Labov's (2007) notions of transmission, diffusion, and incrementation. Labov determined that parent-to-child transmission faithfully reproduces structural patterns such as the Northern Cities Shift (NCS), but adult-to-adult diffusion does not. NCS is transmitted faithfully to new generations of U.S. Inland North children. But St. Louis speakers, depending only on adult-adult contact, only attain an incomplete, unsystematic version. Labov (2007) attributed the difference to children's superior language-learning ability; transmission and diffusion are categorically different processes in that approach. By contrast, our multiagent simulation suggests that such transmission/diffusion effects can be derived by simple density of interactions and simple exemplar learning; we also find that incrementation is a natural outcome of this model. Unlike Labov (2007), this model does not require a dichotomy between transmission and diffusion. While dichotomous assumptions about child versus adult learning may be necessary in other contexts, our results suggest that the NCS effects in Labov (2007) may be explained economically in terms of simple density of interactions between speakers. Our results also provide an agent-based perspective supporting and explicating the notion of speech community.

Class matters: the sociolinguistics ofgooseandgoatin Manchester English

“…Social class construed in terms of distances between social groups has been shown to be a sufficient and, in fact, better predictor, supporting Labov's (2001:192) conclusion that "the account based on covert attitudes is redundant to the extent that the network daily interaction brings people into contact with the new form in proportion to their distance from the originating group." These findings are also compatible with recent work on mathematical modelling of language change by Kauhanen (2017; see also Stanford & Kenny, 2013) demonstrating ( pace Blythe & Croft, 2012), through computer simulations and mathematical analysis, that change will propagate throughout the community in a well-behaved fashion (i.e., essentially following an S-curve) in the absence of social biases. In other words, it can be modelled successfully just on the basis of the number of connections speakers have in their speech community, without extra social biases.…”

Simulating vowel chain shift in Xumi

“…Naturally, other models using different parameters might offer different explanations of the Xumi data. One could think, for instance, of one-dimensional models for vowel shifts (as in Ettlinger, 2007;and Stanford and Kenny, 2013) or of models based on co-articulation, tone or vowel harmony. Such models might illuminate other facets of Xumi vowel chain shift, thereby furthering our understanding of Xumi, and more generally, enhancing the potential of applying computer modeling to studies of concrete scenarios of change in real-world languages.…”

An experimental study of the role of social factors in language change: The case of loanword adaptations

“…Indeed, the dynamics of social circles have been argued to determine the propagation of sound change 1 (e.g., Milroy and Milroy 1985;Kerswill and Williams 2000;Labov 2001;Trudgill 2004;Stanford and Kenny 2013). Using simulations, Nettle (1999) has even argued that language change can only come about when the social conditions are suitable.…”