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Basque, Frisian, English, Spanish and Dutch How to study multilingualism in non- empirical settings? René J. Jorna (Frisian Academy, KNAW, Leeuwarden;

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Presentation on theme: "Basque, Frisian, English, Spanish and Dutch How to study multilingualism in non- empirical settings? René J. Jorna (Frisian Academy, KNAW, Leeuwarden;"— Presentation transcript:

1 Basque, Frisian, English, Spanish and Dutch How to study multilingualism in non- empirical settings? René J. Jorna (Frisian Academy, KNAW, Leeuwarden; University of Groningen, The Netherlands) Email

2 Structure 1. Introduction and problem statement 2. Existing models of multilingualism a. Mathematical models b. Multi-agent models c. Problems with existing models 3. Alternative model: Multi-actor systems with cognitively plausible actors (MAS) 4. The example of CROSS; towards MAS-ML 5. Questions to be answered by MAS-ML 6. Discussion and Future Research 2

3 1. Introduction Given a region with 2 or 3 languages F-D; B-S or B-S-E, etc. 3 The Basque situation The Frisian situation

4 1. Introduction Suppose the languages are spoken by 30%- 70% or 50%-50% of the population, etc. Suppose B is dominant; or B and S are equivalent Suppose policy change from D into F and F only or E is introduced annex to B and S. What happens psychologically, sociologically or institutionally to dominance, vitality, etc. of B, S and E or D, F and E? 4

5 1. Introduction Many ideological remarks can be made on B, S, D, F and D. What about empirical evidence? What does theory say about long term effects of multi language learning? of a dominance change; of bi-lingual stimulation? of language vitality and of language death? of the speed of acceptance of a new language E of language users and social cohesion, for example as a result of policy change 5

6 1. Problem Statement Shortcomings of empirical research (in ML) a. linguistically: languages change over time b. ethical: one can not force mono- or bi-lingualism c. philosophically: difficult separation of levels of aggregation of individual, group and institutional; d. psychologically: long time-horizon for language and multi-language learning e. sociologically: long term-horizon of effects of multi-language use (status, acceptance, etc.) In general: long term horizon and mixed levels of aggregation 6

7 1. Problem statement It is therefore difficult to empirically study ML!! Solution: use computer simulation models Caveats (be aware of) if possible accept emergence, if possible use reduction, (chemistry and physics; sociology and psychology; economy and psychology), computer simulation means selection (not everything can be modeled completely), beware of validation, but also of surprise 7

8 2a. Mathematical models (Abrams & Strogatz, 2003) Models (language death, revitalization): Abrams-Strogatz: languages fixed; competing speakers; connected population; speakers are monolingual; languages cannot coexist stably. Plotted against data from 42 regions Language extinction/revitalization (Fernando, Lissa & Goldstein 2005), Bilingualism and language competition (Castello, Toivonen, Eguiliz & San Miguel, 2006) Corrections/completions Abrams-Strogatz model (Staufferl, Castello, Eguiliz & San Miguel, 2006). 8

9 2a. Mathematical models (Troitzsch, 2004) 9

10 2b. Multi agent models (Troitzsch, 2004) 10 After 84 “years” there are 273 bilinguals, 141 German speakers, 121 French speakers, 183 pairs and 273 women, 262 men with the age structure shown at right bottom. The simulation started with 100 agents.

11 2b. Multi agent models (Troitzsch, 2004) 11 Language group proportions in two simulation runs with different probabilities of children adopting their parents’ languages and of different adult learning ag = af = 0.2, learn for partners and neighbors (a= probabilities of children adopting a language (French or German)

12 2b. Multi agent models (Troitzsch, 2004) 12 ag = af = 0.7, learn for partners and neighbors (a = probabilities of children adopting a language German or French (Troitzsch 2004)

13 2c. Criticizing existing models Criticisms on existing models Language seems entity that can exist without some (human) actor knowing and using it However, we see language as an artifact: a human made construct or artifact Humans are sign/symbol learners and users; every simulation model has to deal with humans as cognitive or information processing systems Humans are also social actors; models should include social parameters, such as standardization, authority and mutual adjustment 13

14 3. Alternative models: cognition and language Suppose two actors X and Y Actors live in social environment (Umwelt) Actors use social constructs and signs (e.g., organized in languages) and communicate. Actor X and Y are cognitive actors and have: Architecture: memory, perception, action/motor Content (goals, personal constructs) in terms of mental representations. Actor has one or more languages. 14

15 3. Alternative models A Cognitive Multi-Actor system 15

16 4 Example of a MAS: CROSS How to study riots and masses: Crowd Sim. of Situated Ind. (CROSS; Wijermans, 2011) 16 a: gathering b: stage pop concert c: political meeting d: going to toilets


18 4. Example of a MAS: CROSS (group, individual and intra-individual)

19 4. Example of a MAS: CROSS (intra-individual = cognitive) 19

20 4. Example of a MAS: CROSS (visualization of the simulation) Colors: kinds of individuals Y: music lover B: drinker G: idol lover etc

21 4. From CROSS to MAS-ML CROSS is not about language MAS-ML is solution to study various levels of aggregation (psych., sociolo., institutionally) The simulation will be built in software tools as REPAST, NETLOGO, etc. This can answer various research questions: under what conditions will Frisian get extinct? what policy will make Basque the only language in the region? 21

22 4. MAS-ML: individual and : intra or cognitive part L1 and L2 22

23 4. MAS-ML: Specifications of MAS/actor details Actor is cognitively plausible (we therefore use “actor” not “agent”; agent is empty entity) We use FIPA specification (Foundation for Intelligent, Physical Agents/Actors) We work with Message transport service (FIPA Agent Message Transport Service Specification) that ensures sending/receiving messages between actors in the actor platform. 23

24 4. MAS-ML: A technical platform for a MAS (group, individual and intra) 24

25 4. MAS-ML: Technical specification of cognitive (intra) structure of L1 actor 25

26 4. MAS-ML: components and multilingual (ML) situations MAS components (cognitive actors) in study ML: Conceptual specification of the issue: e.g., dominance; Extended description of cognitive actor (Cogn. Science); Functional/technical specification setup of model; Experimental setup: e.g., four multilingual situations All actors use L1 and L2, One actor uses L1 and L2 and the others use L1, All actors only use L1 One actor uses L1 and the others use L1 and L2. 26

27 4. MAS-ML environment with various proportions of L1, L2 actors 27

28 5. Questions for multi-language simulations Under what conditions of actors using Basque and Spanish will Spanish become dominant over Basque? (6a; language survival/death) How many actors are necessary to keep Basque existing? Compare all multilingual (6a) with some are only monolingual (6d) Is communication speed different when we compare bilingual actors in a society with a society of monolingual actors? (6a and 6c) 28

29 6. Conclusion and discussion Mathematical models and older multi-agent models do not model that language basicallly is a human cognitive activity. We also need to model and include human cognition! Within MAS-ML actors are carriers of languages. Dynamics can be studied at the individual level (e.g., language learning and interpretation) and at the group level (e.g., language interaction or status ascription). 29

30 6. Future research Implementation of initial prototype (2011) Theoretical extension of handling multilingualism in memory (2011) E.g., retrieval of dominant versus non-dominant language Positioning of social parameters and coordination mechanisms (2012) Operationalization for example for (2012 ) Dominance Status 30

31 31 Memory exercise What about our working memory (STM)

32 32 X V H K A G P D L C

33 33

34 34 L E C H A T E S T S

35 35

36 36 K L M N O P Q R S T

37 37

38 38            

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