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Neutral change1

Published online by Cambridge University Press:  07 June 2016

HENRI KAUHANEN*
Affiliation:
The University of Manchester
*
Author’s address: The University of Manchester, Linguistics and English Language, Samuel Alexander Building, Oxford Road, Manchester, M13 9PL, UKhenri.kauhanen@manchester.ac.uk

Abstract

Language change is neutral if the probability of a language learner adopting any given linguistic variant only depends on the frequency of that variant in the learner’s environment. Ruling out non-neutral motivations of change, be they sociolinguistic, computational, articulatory or functional, a theory of neutral change insists that at least some instances of language change are essentially due to random drift, demographic noise and the social dynamics of finite populations; consequently, it has remained little investigated in the historical and sociolinguistics literature, which has generally been on the lookout for more substantial causes of change. Indeed, recent computational studies have argued that a neutral mechanism cannot give rise to ‘well-behaved’ time series of change which would align with historical data, for instance to generate S-curves. In this paper, I point out a methodological shortcoming of those studies and introduce a mathematical model of neutral change which represents the language community as a dynamic, evolving network of speakers. With computer simulations and a quantitative operationalization of what it means for change to be well-behaved, I show that this model exhibits well-behaved neutral change provided that the language community is suitably clusterized. Thus, neutral change is not only possible but is in fact a characteristic emergent property of a class of social networks. From a theoretical point of view, this finding implies that neutral theories of change deserve more (serious) consideration than they have traditionally received in diachronic and variationist linguistics. Methodologically, it urges that if change is to be successfully modelled, some of the traditional idealizing assumptions employed in much mathematical modelling must be done away with.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2016 

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Footnotes

[1] I thank Ricardo Bermúdez-Otero, David Denison, Tobias Galla and George Walkden for numerous discussions which have contributed greatly to this paper; Laurel MacKenzie, Alan McKane, Mark Muldoon and three anonymous Journal of Linguistics reviewers for comments which resulted in important improvements; audiences at the Student Conference in Complexity Science 2014 (Sussex), the Manchester Forum in Linguistics 2014 (Manchester), the International Conference on Computational Social Science 2015 (Aalto University), the 2015 Meeting of the Linguistics Association of Great Britain (UCL) and the Theory Club of the Cognitive Science Unit at the University of Helsinki, as well as Fernanda Barrientos, Deepthi Gopal, Michaela Hejná and Yuni Kim for feedback; the Faculty of Engineering and Physical Sciences at The University of Manchester for CPU time; and the School of Arts, Languages and Cultures, University of Manchester, and Emil Aaltonen Foundation for financial support.

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