Cover not available
Part of
Advances in Interdisciplinary Language Policy
Edited by François Grin, László Marácz and Nike K. Pokorn
[Studies in World Language Problems 9] 2022
► pp. 381–404

Chapter 19
Complexity in language matters

Concept and uses of agent-based modelling

| University of Geneva

Agent-based modelling belongs to the wider category of computational modelling and is used to replicate real-world social or physical systems and simulate their behaviour in a computer environment, with a view to studying the rules that govern them. Although agent-based modelling has quickly gained recognition in the natural sciences, social scientists only started to explore its potential in the last few decades. This trend has gained momentum over the last ten years, and applications of agent-based modelling to the social sciences have been steadily increasing. Researchers dealing with complex phenomena (in both the social and natural sciences) are increasingly switching from purely analytical (i.e. mathematical) approaches (which often attempt to find a ‘closed-form solution’ to a problem) to computation-based approaches (which rely on algorithms and simulations). Whereas applications of analytical methods to study language-related issues have a long-standing tradition, systematic adoption of a complexity-based view of language matters and application of computational methods have yet to establish themselves. However, recent research in language studies shows attempts to address the complexity of language-related issues in a more systematic way. Particular attention is devoted to the fact that such issues involve numerous agents and variables, and causal links between these variables are often non-linear. Although it may be challenging to model such systems through equations, agent-based modelling offers a natural solution, in that it provides an easy way of replicating various degrees of diversity in a computer environment. These models can then be run many times with various settings to study the system’s short-term and long-term behaviour and the way it responds to different initial conditions. Agent-based modelling lends itself very well both to studies that investigate the nature and functioning of languages and to sociolinguistic studies that focus on language-mediated interactions between individuals.

Article outline
  • 1.Introduction
  • 2.Agent-based modelling and the MIME trade-off
  • 3.Key concepts and tools
  • 4.Complexity in society and languages
    • 4.1An intuitive definition of complexity
    • 4.2A technical definition of complexity
      • 4.2.1Entropy and CAS
    • 4.3Computational social science
      • 4.3.1Studying complexity in practice: Agent-based modelling
    • 4.4Complexity and languages
      • 4.4.1Complexity in linguistic features
      • 4.4.2Complexity in sociolinguistic systems
  • 5.Discussion
    • 5.1An ABM of grammatical agreement
    • 5.2An ABM of language competition
    • 5.3Insights from MIME research
  • 6.Conclusion
  • Notes
  • References
Published online: 21 January 2022
DOI logo
https://doi.org/10.1075/wlp.9.19civ
References
Ambrose, John & Williams, Colin
(1981) ‘On the Spatial Definition of Minority. Scale as an Influence in the Geolinguistic Analysis of Welsh’, in E. Haugen, D. Thomson, and J. D. McClure (Eds.), Minority Languages Today (pp. 53–71). Edinburgh: Edinburgh University Press.Google Scholar
Bandini, Stefania, Manzoni, Sara & Vizzari, Giuseppe
(2009) Agent Based Modeling and Simulation: An Informatics Perspective. Journal of Artificial Societies and Social Simulation 12(4), 4.Google Scholar
Barrow-Green, June
(2008) ‘The Three-Body Problem’, in T. Gowers, J. Barrow-Green & L. Imre (Eds.), The Princeton Companion to Mathematics (pp. 726–728). Princeton: Princeton University Press.Google Scholar
Batty, Michael
(2009) ‘Cities as Complex Systems: Scaling, Interaction, Networks, Dynamics and Urban Morphologies’, in R. Meyers (Ed.), Encyclopedia of Complexity and Systems Science (pp. 1041–1071). New York: Springer. DOI logoGoogle Scholar
Beuls, Katrien & Steels, Luc
(2013) Agent-based models of strategies for the emergence and evolution of grammatical agreement. PLoS ONE 8(3), e58960. DOI logoGoogle Scholar
Castelló, Xavier, Loureiro-Porto, Lucía & San Miguel, Maxi
(2013) Agent-based models of language competition. International Journal of the Sociology of Language 221, 21–51. DOI logoGoogle Scholar
Chen, Shu-Heng & Yu, Tina
(2018) ‘Big Data in Computational Social Sciences and Humanities: an Introduction’, in S. Chen (Ed.), Big Data in Computational Social Science and Humanities (pp. 1–25). Springer International Publishing. DOI logoGoogle Scholar
Cilliers, Paul
(1998) Complexity and postmodernism: Understanding complex systems. London: Routledge.Google Scholar
Cioffi-Rivella, Claudio
(2017) Introduction to Computational Social Science. Principles and Applications. Cham: Springer. DOI logoGoogle Scholar
Civico, Marco
(2019a) The complexity of knowledge sharing in multilingual corporations: evidence from agent-based simulations. Empirica 46(4), 767–795. DOI logoGoogle Scholar
(2019b) The Dynamics of Language Minorities: Evidence from an Agent-Based Model of Language Contact. Journal of Artificial Societies and Social Simulation 22(4), 3. DOI logoGoogle Scholar
(2021) Simulating language knowledge across the EU: language regimes, language learning and consequences for linguistic disenfranchisement. Journal of Computational Social Science (2021) DOI logoGoogle Scholar
Dixon, Robert
(1996) Australian Languages. Cambridge: Cambridge University Press.Google Scholar
Epstein, Joshua
(1999) Agent-Based Computational Models and Generative Social Science. Complexity 4(5), 41–60. DOI logoGoogle Scholar
Grin, François
(1992) Towards a Threshold Theory of Minority Language Survival. Kyklos 45(1), 69–97. DOI logoGoogle Scholar
Hamilton, David & Trolier, Tina
(1986) ‘Stereotypes and stereotyping: an overview of the cognitive approach’, in J. F. Dovidio & S. L. Gaertner (Eds.), Prejudice, discrimination, and racism (pp. 127–163). Orlando: Academic Press.Google Scholar
Herbert, Simon
(1955) A Behavioral Model of Rational Choice. The Quarterly Journal of Economics 69(1), 99–118. DOI logoGoogle Scholar
Hunter, Elizabeth, Mac Namee, Brian & Kelleher, John D.
(2017) A Taxonomy for Agent-Based Models in Human Infectious Disease Epidemiology. Journal of Artificial Societies and Social Simulation 20(3), 2. DOI logoGoogle Scholar
Kilpatrick, Henry
(2001) Complexity, spontaneous order, and Friedrich Hayek: Are spontaneous order and complexity essentially the same thing? Complexity 6(1), 16–20. DOI logoGoogle Scholar
Larsen-Freeman, Diane & Cameron, Lynne
(2008) Complex Systems and Applied Linguistics. Oxford: Oxford University Press.Google Scholar
Lazer, David et al.
(2009) Computational Social Science. Social Science 323(5915), 721–723.Google Scholar
Lippmann, Walter
(1922) Public Opinion. New York: Harcourt, Brace and Company.Google Scholar
Manning, Chris & Schütze, Hinrich
(1999) Foundations of Statistical Natural Language Processing. Cambridge: MIT Press.Google Scholar
Marschak, Jakob
(1965) Economics of Language. Behavioral Science 10(2), 135–140. DOI logoGoogle Scholar
Maturana, Humberto & Varela, Francisco
(1972) De máquinas y seres vivos. Santiago: Editorial Universitaria.Google Scholar
Mitchell, Melanie
(2009) Complexity: a guided tour. Oxford: Oxford University Press.Google Scholar
Montemurro, Marcelo & Zanette, Damián
(2011) Universal Entropy of Word Ordering Across Linguistic Families. PLoS ONE 6(5), e19875. DOI logoGoogle Scholar
Prigogine, Ilya
(1977) Time, structure and fluctuations. Nobel lecture 8 December 1977.Google Scholar
Rehr, David
(1992) In Hayek’s legacy of the spontaneous order. Federal Reserve Bank of Minneapolis Bulletin, June 1992.Google Scholar
Rickles, Dean, Hawe, Penelope & Shiell, Alan
(2007) A simple guide to chaos and complexity. Journal of Epidemiology and Community Health 61(11), 933–937. DOI logoGoogle Scholar
Room, Graham
(2011) Complexity, Institutions and Public Policy Agile Decision-Making in a Turbulent World. Cheltenham: Edward Elgar. DOI logoGoogle Scholar
Shannon, Claude
(1948) A Mathematical Theory of Communication. Bell System Technical Journal 27(3), 379–423. DOI logoGoogle Scholar
Thaler, Richard
(2000) From Homo economicus to Homo sapiens. Journal of Economic Perspectives 14(1), 133–141. DOI logoGoogle Scholar
Wilensky, Uri & Rand, William
(2015) An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. London & Cambridge: The MIT Press.Google Scholar
Zhou, Jianfeng, Lou, Yang, Chen, Guanrong & Tang, Wallace
(2018) Multi-language naming game. Physica A 496, 620–634. DOI logoGoogle Scholar
Zou, Yawen & Chen, Shu-Heng
(2018) ‘Has Homo economicus Evolved into Homo sapiens from 1992 to 2014: What Does Corpus Linguistics Say?’, in S. Chen (Ed.), Big Data in Computational Social Science and Humanities (pp. 117–137). Springer International Publishing. DOI logoGoogle Scholar