This chapter aimed at, first, reviewing complexity and, second, showing that the multilingual challenge and language issues in general qualify to be con-sidered complex matters. Therefore, it is legitimate to apply a complex ap-proach to language matters. I have shown that language issues display non-linear behaviours, featuring feedback loops, spontaneous order and emer-gence. Besides, extreme and unlikely events can have dramatic repercus-sions. Consequently, policies dealing with language issues should be drafted adopting a specific complexity approach. This is particularly true consid-ering that simulations are a good substitute for real-life experiments when these are potentially expensive and burdensome.
The literature on complexity theory offers a good number of examples of applications of complexity theory to public policy matters. However, com-plexity theory is only seldom applied to language policies. Therefore, as of today there is no such thing as a complex framework to implement language policies, able to address language challenges in a flexible and adaptive way, taking all non-trivial aspects into consideration. In particular, in the coming chapters I adopt an agent-based modelling and sensitivity analysis approach.
The general idea is that, due to the non-negligible presence of randomness, language policies (and, in general, policies addressing situations where the future is unpredictable) call for a complex approach. Therefore, traditional quantitative and qualitative methods need to be complemented by other re-search methods, such as computer-based simulations. Agent-based models have the great advantage of relating the heterogeneous micro-behaviours of agents58 with different information, decision rules, and situations to the macro-behaviour of the overall system (Lempert, 2002). My objective is to show that a complex framework is a valuable ally for policy makers. I present some traditional language policy issues and show how they can be studied from a complexity theory perspective. I run simulations in different contexts calibrating the simulators according to different inputs so as to answer ques-tions such as: given a certain set of initial condiques-tions, what is the long term tendency of language use? How does it change following specific kinds of interventions?
58I shall clarify here that, throughout this dissertation, I the use the word "agent" as a synonym of "actor" in a simulation model, as is customary in the relevant literature.
Chapter 2
The Complexity of Knowledge Sharing in Multilingual
Corporations: Evidence from Agent-Based Simulations
2.1 Introduction
As was discussed in depth in Chapter1, in the past few decades, scholars of all sciences have been paying increasing attention to aspects of complexity that characterize matters of both nature and society, which gave an interdis-ciplinary spin to much of the research effort reflected in the literature. The world has always been a complex place, but people were on average less aware of this fact. In his famous book "Turbulence in World Politics: a The-ory of Change and Continuity", Rosenau (1990) argues that, until the 50s, people were on average less educated and in general less concerned with global issues as a consequence of limited information availability. As edu-cation and access to information have both dramatically increased, people have become increasingly aware of global issues. This has led to the some-what deceptive impression that, over the decades, the world has become a more complex place. Nevertheless, social systems have indeed become ar-guably more complex, or at least more complicated. Indeed, they are much more interconnected today than they used to be in the past as a result of the massive progress in information and communication technologies. Scholars in the management and economic sciences have been exploring the issue of complexity since the early 1990s.
In this chapter I argue that complexity theory and agent-based modelling can provide a great support to the research on language issues. This was
quickly mentioned in Chapter 1 and will be discussed in greater detail in this (and the following) chapters. In particular, in this chapter I explore the potential application of agent-based modelling to the study of communica-tion in multilingual workplaces. I analyse the features of communicacommunica-tion within multilingual businesses to show that it is an intrinsically complex is-sue and that it deserves being studied by means of the tools and concepts of complexity theory. In particular, using the software NetLogo, I develop an agent-based model (ABM) that simulates different scenarios, in order to detect macro-dynamics generated by different individual micro-behaviours and corporate policies. First, I review the ideas of complexity and language problems. Then, I present multinational corporations as multilingual com-plex entities. Finally, I concentrate on the processes of knowledge sharing, knowledge creation and knowledge accumulation within a multilingual work-ing environment.1 In particular, I focus on the potentially unbalanced accu-mulation of knowledge across different language groups within corporations as a function of language skills.
The objective of this chapter is first and foremost to explore the potential of agent-based modelling as a tool of complexity theory to study the dynam-ics of language systems. As a matter of fact, ABMs have been used exten-sively to explore all sorts of social phenomena in the past few years. Never-theless, they have only been rarely applied to language issues, and virtually never to issues of language policy. My objectives are therefore to develop an ABM of communication in a multilingual context and to show that ABMs lend themselves very well to the analysis of language matters. Besides, I will discuss the limitations of the model presented here and how they could be addressed in the future. In general, this chapter wishes to contribute to the fields of language policy and language economics by expanding its research methodologies. Although this is mostly a theoretical discussion, whenever possible, I will try to contextualize my analysis with examples from Europe or the European Union. In particular, I draw on several cases from the Swiss context to provide validation to the model. The reason behind this choice lies in the fact that Switzerland, being a multilingual country, provides numerous examples that match well the underlying assumptions of the model.
1As 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.