Abstract
This article discusses the possibility of adopting a complexity theory approach to the study of language policy and planning (LPP). Besides, it argues that agent-based modelling provides a significant support in this sense. Indeed, while agent-based modelling has become a major ally of researchers in the social sciences, it remains largely unexploited in the study of language-related issues in society. As a central tool of complexity theory, agent-based models (ABMs) lend themselves particularly well to the study of all sorts of complex systems. To provide justification for the use of ABMs in LPP, I show how language issues display the typical traits of complex systems and how ABMs can easily translate ideas and notions from the literature into computer-simulated processes. To support my argument, I discuss communication within multinational corporations as an example of a highly complex language matter. In particular, I focus on how language skills impact the process of knowledge creation and knowledge sharing among employees. By means of a model based on a number of straightforward rules, I show how poor language skills (or an utter lack thereof) risks creating an unbalanced distribution of knowledge (and, consequently, of power) across language groups and how this unbalanced distribution is very sensitive to initial conditions. On the contrary, average language skills seem to support communication well enough to avoid skews that favour even slightly more numerous language groups.
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Notes
As is customary, I will use the word “multilingual” to refer to a context where several languages are spoken and “plurilingual” to describe individuals able to speak more than one language.
This clearly does not mean that this is the case everywhere. Many areas, especially outside large urban conglomerates, have remained to a great extent culturally and linguistically homogeneous or only have a small allophone population.
For an in-depth review of the characteristics of complex systems, see Mitchell (2009).
To show to what extent doing business with customers can be an extremely multilingual activity, Tietze et al. (2016) mention a study from the University of Manchester discussing, among other things, how commercial signage in Chinese, Urdu, Polish, Arabic and Bengali and other languages is a common business practice within the city of Manchester.
Clearly, this remark only refers to those languages that have an official status in South America, de facto or de iure, the most recurring being Spanish and Portuguese, followed by English, French, Dutch, and, to a lesser extent, Guarani. Autochthonous languages of South America only have regional recognition, if any. The only notable exception to this is Bolivia, whose constitution explicitly lists Spanish along with 36 indigenous languages (some of which even extinct) as official. Besides, it should be noted that the multilingual nature of the European common market is a consequence of the fact that Europe has been a multilingual continent long before any integration process was started, and not the other way around. Each small expansion of the European Union (and of the common market) came almost systematically with the addition of at least one new language.
In this regard, there seems to be a certain awareness of the psychological and power implications of language imbalance. Bruntse (2003) discusses the case of the Danish-Norwegian-Swedish airline SAS which explicitly opted for an informal mix of the three languages (known as “SASperanto”) as a working language rather than specifically one of them in order to avoid power imbalances. This case is an interesting example of intercomprehension. For more on this, see Sect. 6.
In short, diglossia refers to those situations in which two languages are spoken within a community for different purposes (such as Swiss German and Standard German in German-speaking Switzerland). Conversely, bilingualism describes those situations in which two languages are spoken within a community with no differences in terms of purposes or prestige (such as French and Dutch in Belgium). Diglossia and bilingualism are not mutually exclusive. For more on this, see Fishman (1967). Interestingly, diglossia and bilingualism (whose definitions are much more articulated then presented here) provide fertile ground for the development of an ad hoc agent-based model. Indeed, both diglossia and bilingualism can be broken down into a number of individual properties and behavioural rules (such as the frequency with which one language is used rather than the other, the perceived status of each language, and the level of fluency in each language) that then bring about results at community level. We could also go as far as to argue that diglossia and (societal) bilingualism could be conceived as “emergent properties” of individual bilingualism combined with a specific set of behaviours. Systems are said to display “emergence” when they exhibit novel properties that cannot be traced back to their individual components (Homer-Dixon 2010), but that result from their interaction. This discussion, however, is left for future research.
For more on the classification of language policies, see Grin and Civico (2018).
Absorptive capacity is defined by Levinthal and Cohen (1990) as “[the] ability to recognize the value of new information, assimilate it, and apply it to commercial end.”
It should be noted that his definition of language is not strict and includes, for example, statistical and accounting indicators.
For a thorough review of the different ways of defining knowledge, see Venzin and Georg von Krogh (1998).
For the full text of the law, see https://www.admin.ch/opc/fr/classified-compilation/20062545/index.html.
My translation.
For the full text of the law, see https://www.admin.ch/opc/en/classified-compilation/20101351/index.html.
The part of the code concerning the grouping motion is largely based on the “Grouping Turtles Example” model developed by Uri Wilensky and available in the library of the NetLogo software.
As has been stressed, the goal of this article is not to make predictions about how much new knowledge is created within multilingual corporations. My goal here is to look at different language strategies and how they compare in the process of knowledge sharing and accumulation, beyond exploring the potential of ABMs to study language-related issues. As a consequence, it is not crucial to have accurate numbers, as long as the way in which knowledge creation happens is reasonable.
This case is obviously only theoretical, in that in a two-person group, LC has to be the language of at least one of them, based on the rules explained above.
On this topic, it is worth mentioning Iannàccaro et al. (2018), who distinguish between linguistic insecurity and linguistic unease. The former refers to a feeling of perceived inadequacy of one’s own language variety with respect to a socially expected standard norm. The latter refers to a “set of situation[s] in which the speaker’s linguistic knowledge is not adequate to the linguistic needs of the moment,” that is, whenever the speaker feels that her linguistic competence is not adequate for the linguistic task that she needs to perform. In our analysis, this would mean that her knowledge of the language is not enough to support her communicative needs, therefore causing a sense of unease that affects her communicative performance beyond her limited language skills.
With 240 employees and an arbitrary maximum value of knowledge maturity of 100, the maximum total knowledge that can be possibly achieved is 24,000.
Note that the scale in the first two graphs of the first and the second simulation is different from the others for legibility reasons.
The Greenberg index is one of the simplest metrics used to compute the degree of linguistic diversity. It is also referred to as the linguistic diversity index and was put forward by Greenberg (1956). If we consider a community where n languages (with n > 1) are spoken, each individual speaks only one language, and the proportion of speakers of language i is pi (with i = 1, 2, …, n and 0 < pi < 1), the total probability of randomly picking two individuals who have the same L1 is given by the sum of this event happening for each single language (i.e., picking two individuals whose native language is 1, two individuals whose native language is 2, and so on), that is:
$$\mathop \sum \limits_{i = 1}^{n} p_{i}^{2} = p_{1}^{2} + p_{2}^{2} + \cdots + p_{n}^{2}$$The Greenberg index, being a metric of diversity rather than uniformity, is then equal to:
$$G = 1 - \mathop \sum \limits_{i = 1}^{n} p_{i}^{2}$$It should be noted that, although intercomprehension is often emphasized between related languages (for example, between Romance or Scandinavian languages), a degree of mutual intelligibility between the two languages is not a necessary condition. Indeed, intercomprehension can also rely on previously acquired receptive knowledge of the other language. For more on the topic of intercomprehension in the context of multilingual organisations, see Grin (2008).
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Funding
The research leading to these results has received funding from the European Community’s Seventh Framework Programme under grant agreement No. 613344 (MIME Project).
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Civico, M. The complexity of knowledge sharing in multilingual corporations: evidence from agent-based simulations. Empirica 46, 767–795 (2019). https://doi.org/10.1007/s10663-019-09435-8
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DOI: https://doi.org/10.1007/s10663-019-09435-8