Complexity as a Paradigm for Social Sciences and Linguistics:

Theoretical Basis and Perspectives

Gemma Bel-Enguix

1 a

Angels Massip-Bonet

2 b

and Gerardo Sierra

1 c

Instituto de Ingenier

ıa, Universidad Nacional Aut

onoma de Mexico, Ciudad de Mexico, Mexico

Departament de Llengua i Literatura Catalanes, Universitat de Barcelona, Barcelona, Catalunya, Spain

Keywords:

Complexity, Complex Systems, Scientiﬁc Paradigm, Quantitative and Qualitative Methods, Natural Language

as a CAS.

Abstract:

This article discusses the relevance and signiﬁcance of the use of complexity as a scientiﬁc paradigm in

social and human sciences, focusing on linguistics. For this, a review of the concept of paradigm is made,

and its evolution in the last decades. In this framework, the controversy between quantitative and qualitative

methods and their validity in the twentieth century is discussed. In this dichotomy, we claim that the theory

of complexity is prepared to assume the use of the so-called Mixed Methods Research (MMR). The paper

develops the impact of Complex Systems (CS) and Complex Adaptive systems (CAS) in science, as well as

the epistemological and methodological implications this entails. Moreover, natural language is deﬁned as

a CAS. In general, the article defends the adoption of this paradigm in linguistics, both in synchronous and

diachronic research, providing some examples of these new lines of study. In spite of the still emerging nature

of some formulations, we envision a deep theoretical breakthrough in linguistics thanks to this interdisciplinary

perspective.

1 INTRODUCTION

Tomas Kuhn published in the sixties his inﬂuential

essay The structure of scientiﬁc revolutions (1962).

In this book, the philosopher argues that the underly-

ing mechanics of scientiﬁc revolutions is a paradigm

shift, understanding paradigm as a set of circum-

stances and intellectual possibilities that favor a dif-

ferent way of seeing the world and the phenomena

under investigation. This idea is somehow opposed

to Popper’s theory (Popper, 1962), that states that sci-

ence advances by accumulation, through falsiﬁability,

discarding laws that contradict experience.

The idea of paradigm established a new way of

standing before the dogmas of knowledge, and en-

couraged reﬂection on the philosophy of science and

scientiﬁc methodology. However, the deﬁnition of the

concept is not easy, and Kuhn himself seems to have

used it with about twenty different meanings (Mas-

terman, 1970). Guba and Lincoln (1994) understand

the term as a set of basic beliefs that have to do with

https://orcid.org/0000-0002-1411-5736

https://orcid.org/0000-0001-6845-2407

https://orcid.org/0000-0002-6724-1090

the last and ﬁrst principles, and that cannot be proven

in a conventional sense of the word. Mertens (2005)

thinks that it can be deﬁned as a way of looking at

the world, composed of certain guiding philosoph-

ical assumptions, direct thinking and action. Neu-

man (2006) refers to a paradigm as a general orga-

nizational framework for theory and research that in-

cludes basic assumptions, key issues, research qual-

ity models and methods of ﬁnding answers. Denzin

and Lincoln (2005), meanwhile, describe a paradigm

as a network that contains the epistemological, onto-

logical and methodological premises of a researcher.

It is understood, then, that all research is interpretive

and is guided by a set of beliefs and feelings of a re-

searcher about the world and how it should be under-

stood and studied.

An issue that has involved some controversy re-

garding scientiﬁc paradigms is the theoretical debate

on methodology. Some authors (Guba and Lincoln,

1988) wonder if each of the research paradigms nec-

essarily implies research methodologies. That is, they

wonder about how a paradigm is structured, what its

components are. In this regard, Guba (1990) him-

self makes relevant contributions in subsequent years.

In the aforementioned work, the characterizes the re-

136

Bel-Enguix, G., Massip-Bonet, Á. and Sierra, G.

Complexity as a Paradigm for Social Sciences and Linguistics: Theoretical Basis and Perspectives.

DOI: 10.5220/0009579901360142

In Proceedings of the 5th International Conference on Complexity, Future Information Systems and Risk (COMPLEXIS 2020), pages 136-142

ISBN: 978-989-758-427-5

search in relation to the positioning about three as-

pects: the ontological - what is the nature of reality or

of the cognizable -, the epistemological - what is the

nature of the relationship between the cognitive and

the cognizable - and the methodological - how the

cognitive should proceed to apprehend knowledge.

According to this position, the methodological aspect

is constituent of the paradigm.

One of the most controversial debates in the area

of Social Sciences and Humanities has been the di-

chotomy quantitative/qualitative. From the perspec-

tive of Guba (1990), the use of quantitative or quali-

tative methods is necessarily determined by the philo-

sophical and epistemological options in which the

project is framed or the researcher militates.

In the last years of the 20th Century, and in the

ﬁrst decade of the new millennium, several research

paradigms are coexisting. Guba (1990) noted that the

traditional framework of science since the nineteenth

century is positivism, although this has been com-

pleted (or replaced) in recent years by three alterna-

tive scientiﬁc trends: post-positivism, critical theory

and constructivism. However, the arising of new per-

spectives has been beyond this paradigmatic substitu-

tion. The age of communication is also the age of the

‘multi’ as a transversal idea in every ﬁeld of knowl-

edge. Eclecticism and coexistence of different views

of the world and science is one of the most prominent

characteristics of the contemporary societies.

Among the main pivotal ideas that have arisen in

science in the last twenty years, one of the most con-

solidated is that of complexity, that can be seen as a

multidisciplinary platform with the capability of hav-

ing an impact in every area of knowledge.

In the following, we explain the emergence and

theoretical bases the paradigm (Section 2). In Sec-

tion 3 the debate between quantitative and qualitative

methods is introduced. We also provide some ideas on

how these could be integrated in complexity. Section

4 deals with the consideration of natural language as

complex system. Additionally, we give some exam-

ples of branches of linguistics that have adopted this

perspective. Finally, Section 5 shows why we support

this new scientiﬁc position to study natural language

as well as social sciences.

2 COMPLEXITY AS A

PARADIGM

The modern concept of complexity in science has its

roots in the advances that occurred in the twentieth

century in some pilot disciplines (Marcus, 1974), such

as biology and physics. In addition, in the same pe-

riod, the foundations of cybernetics (Wiener, 1990;

Ashby, 1956) and systems theory (von Bertalanffy,

1968) were laid, two disciplines that can be consid-

ered transversal.

The contributions in these disciplines had an im-

pact in social sciences in the seventies, when authors

like Gregory Bateson (1972) and Edgar Morin Morin

(1973) took the theories introduced by cybernetics

and systems theory to the ﬁelds of philosophy and

communication.

The term ‘complex systems’ arose in the late 70s

from the systems theory, although it was not extended

as a research area until the 90s, when the Santa Fe

Institute

was created. In one of the ﬁrst works on

the topic, Vemuri (1978), attempted an extensional

deﬁnition, based on characteristics: large number of

components, dynamicity and emergency. Some years

later Gell-Mann (1995) made a ﬁrst step for consider-

ing complexity an interdisciplinary paradigm for the

study of both, natural and artiﬁcial entities, by going

deeper into the structure and working of those sys-

tems. His deﬁnition followed the same pattern that

Vemuri’s, highlighting the following common fea-

tures: large number of components, interaction, self-

organization, emergency, non-linear behavior and de-

pendence on previous actions.

At the beginning of 21st century Bar-Yam (2002)

deﬁned complex systems as a new discipline that ap-

proaches how parts of a system and their relation-

ships give rise to the collective behaviors of the sys-

tem, and how the system interrelates with its environ-

ment. From this deﬁnition, a large number of com-

plex systems can be perceived, both in the domain of

social sciences, as in biology or physics. Societies,

the brain with its neural connections, water, weather,

swarms, the immune system... can be examples of

this phenomenon. But also some artiﬁcial construc-

tions, such as trafﬁc, internet communications or the

economic network. In theory, then, complex systems

offer a clearly transversal scientiﬁc perspective, with

a possibility of methodological exchange and mul-

tidisciplinarity. Complexity offers an epistemologi-

cal framework for science in the information society,

where interconnection is a key element, and all re-

search areas inﬂuence each other.

In this new scientiﬁc scenario, contributions from

different areas of knowledge converge: philosophy

and complex thinking (Morin, 2008), physics (Bohm,

1980; Prigogine and Stengers, 1992), biology (Matu-

rana and Varela, 2004), and ecology (Allen and Hoek-

stra, 1992).

It seems that complexity has all the ingredients to

have been understood as a research paradigm in the

https://www.santafe.edu/

Complexity as a Paradigm for Social Sciences and Linguistics: Theoretical Basis and Perspectives

137

Kuhn sense. It epistemologically ﬁts to the view of

seeing the world of the 21st century. In this vein,

it can provide a transdisciplinary umbrella for many

phenomena that today are not perceived as they were

some years ago. It has the concepts, methods and ob-

jectives that can give unity to the research.

The development of this concept has also implied

the emergence of some common transdisciplinary

methods of research. One of the best examples is the

theory of networks (Barab

asi and Albert, 1999; Sol

2009), a crucial technique in this framework. This

method is directly related to the two pilot sciences of

complexity, physics and biology.

Two more methodological tools that complex sys-

tems have integrated are statistics and data science.

Both disciplines are commonly used nowadays for

analysis in both social and formal sciences.

The nature of the techniques adopted by complex-

ity, chieﬂy taken from physics and statistics, could

cause that it has mainly linked with what has been

called, in social sciences, the quantitative paradigm.

This has entailed the rejection of some researchers,

that align themselves with the qualitative framework.

In Section 3 the problem will be approached.

3 COMPLEXITY AND

QUANTITATIVE RESEARCH

As seen in Section 1, some authors include the

methodological option as a substantial part of the

paradigms. This view is especially present in social

sciences and linguistics. An extreme example is the

case of Bryman (1990), who states that in sociology

there are two main paradigms, which depend on the

adoption of quantitative or qualitative methods in re-

search.

The epistemological confrontation between the re-

searchers according to the use of quantitative or qual-

itative research methods was very much alive during

the 80s, and has been called “paradigm warfare”. In

this war, various theoretical positions can be found,

the extremes of which are occupied by the ‘purists’,

and the ‘pragmatists’, while the ‘situationalists’ are

placed in an intermediate position.

The ‘purists’ argue that methods cannot be mixed,

since they are immeasurable, in the sense that Khun

associates with the paradigms. The defenders of this

idea are, among other authors, Bryman (1990) and

Smith (1998).

At the other extreme, the ’pragmatists’ argue that

the dichotomy between the quantitative and qualita-

tive research is false, and advocate for the coordinated

and efﬁcient use of both approaches (Tashakkori and

Teddlie (2010); Johnson and Onwuegbuzie (2004);

Smith (2011).

The ‘situationalists’ are in the middle of these two

positions, arguing that some methods can be used in

speciﬁc situations. Ort

ı (1995) and Serrano et al.

(2009), among other authors, support this perspective.

Non-purist positions ﬁnd their philosophical justi-

ﬁcation in Guba and Lincoln (1994), who assume that

the methodological option is not part of the paradigm,

and therefore does not distort the set of fundamental

beliefs of the system. To do this, they propose to make

a distinction between paradigm, strategy, methodol-

ogy and data analysis for research in social sciences,

behavioral sciences and human sciences. From this

perspective, quantitative or qualitative methods can ﬁt

into different perspectives, since method questions are

not nuclear.

Note the evolution suffered between Guba (1990)

and Guba and Lincoln (1994), that is somehow illus-

trative of the theoretical change that social sciences

experimented in the eighties and nineties, and which

also opens the door to end hostilities between quanti-

tative and qualitative approaches.

3.1 Mixed Methods Research (MMR)

The paradigm war (Creswell, 2003), which began

in the eighties and lasted for two decades, ended

up in the early 21st century with what Tashakkori

and Teddlie (2010) called the third methodological

movement, and Mingers (2003) a ceaseﬁre. Finally,

the battle has completely ceased, giving rise to what

Buchanan and Bryman (2007) has come to call the

‘paradigmatic soup’, which is characterized by total

eclecticism.

The agreement to make use of the so-called

‘Mixed Methods Research’ (MMR), and sometimes

‘multi-method’ or ‘integrated’ research (Creswell,

2003), is very strong in the framework of science in

general. In addition, many voices advocate the need

to extend the mixed methodology to the social sci-

ences and to all those disciplines that may have con-

nections with them.

The ﬁrst question posed about MMR is whether

they are a distinctive methodology in itself, or a sim-

ple combination approaches. Dealing with this issue

requires having for a sufﬁciently general deﬁnition,

understanding which scientiﬁc consequences entails

each position, and envisioning the new lines of re-

search that can be opening in each case.

Among the different explanations on how MMR

works, a general one is provided by Creswell and

Plano Clark (2007) who state that MMR are the basis

of a research design with philosophical assumptions

COMPLEXIS 2020 - 5th International Conference on Complexity, Future Information Systems and Risk

138

and methods of information acquisition. The philo-

sophical principles guide the compilation and analy-

sis of data, and the use of both, quantitative and qual-

itative methods, in a study or series of studies. Is this

combined use what provides better perspectives of the

problem.

4 NATURAL LANGUAGE AS A

COMPLEX ENTITY

The inclusion of natural language among complex

systems has its roots in Gell-Mann deﬁnitions of com-

plex systems, seen above (Section 1).

The main features described by Gell-Mann

(1995), were completed with the description of Com-

plex Adaptive Systems (CAS), introduced by Holland

(2006), that include the traits of evolution and adap-

tation to the context. From here, natural language has

started being considered a CAS. One of the ﬁrst de-

scriptions of this can be found in the paper by Beckner

et al. (2009), which highlights the following features

of natural language that link it with CAS: a) multi-

ple agents, b) adaptations (evolution), c) speaker’s be-

haviours are result of the past ones, and have an im-

pact in the future, d) the behaviour of the speakers is

the result of competing factors, from own abilities to

the society, and e) language has emerging structures.

According to this features, natural language shares

many areas of research and methodologies with other

disciplines and, therefore, its inclusion in CAS seems

clear.

However, although natural language complies all

the features of CAS, this problem and perspective has

been repeatedly avoided in linguistics throughout the

twentieth century. For decades, researchers have been

afraid of dealing with the issue, because it can con-

vey ethnic discrimination or other problems beyond

science. Only under the new comprehension of sci-

ence the topic has started to be tackled. In this new

framework, two main ways of dealing with complex-

ity have arisen: differences between two languages,

and differences between historical periods of the same

language, this is, synchronous and diachronic com-

plexity (Andrasson, 2014).

4.1 Types of Linguistic Complexity

Complexity, as conceived by Gell-Mann, (1995)

quantiﬁes the computability of the non-random sys-

tem information, ie. the order introduced by rules, as

the opposite of randomness and disorder: the more

complex the rules, the longer the description of the

regularities.

The treatment of complexity in living languages

can consider all aspects of the system. In principle,

synchrony and diachrony refer to two different ap-

proaches, typological and evolutionary.

A good introduction to typological complexity is

given by Miestamo et al. (2008). In a chapter of this

book, (Miestamo, 2008) the author distinguishes be-

tween absolute and relative complexity. The former

is based on the number of parts of the system, and

follows the idea that the more parts a system has, the

more complex it is. Of course, the concept is not so

simple, and could be expressed in number of inter-

actions, saying that increasing the number of interac-

tions between the components of a system increases

the complexity (Fenk-Oczlon and Fenk, 2008). This

approach has been developed, among others, by Dahl

(2004). The latter is deﬁned in terms of cost and difﬁ-

culty to language users (Miestamo, 2008). This is, the

more difﬁcult a phenomenon is for the user, the more

complex it is. The problem with this approach is that

the difﬁculty is not always the same for all the users or

groups, depending on the social stratum, education or

dialect. Moreover, the different perspective between

speaker and hearer has to be taken into account. Usu-

ally, what is easier for speaker is more difﬁcult for

hearer, and vice versa. Hawkins (2004) is one of the

main authors in the area of relative complexity.

The typological complexity is eminently cross-

lingual, because all comparisons must be established

by comparison between languages. However, the evo-

lutionary approach, is more related to the contrast be-

tween different stages of the same language.

In regards to the evolutionary approach, it assumes

that there is no stable state, in terms of lexical units,

sounds and grammatical rules. As settled in the main

theory of CAS, a stage of the language is directly in-

ﬂuenced by previous stages, and affects the conﬁgu-

rations of the future. Kirby (2002) distinguished three

main areas in language evolution: ontogeny (language

learning), glossogeny (cultural evolution), and phy-

logeny (biological evolution). Among the authors that

have adopted a diachronic perspective can be cited

Narrog and Auwera (2011), Massip-Bonet (2013) and

Mufwene (2013).

Croft (2000) took the methods of the theory

of evolution to explain language change and death.

Some authors that have dealt with the emergence

of linguistic properties (Kirby and Hurford, 2002;

Hutchins and Hazlehurst, 2002), the former from a

clear darwinian perspective, the latter using the con-

cept of auto-organization as the power that guides the

evolution.

The method by excellence of complex approaches

to language emergence and evolution is simulation

Complexity as a Paradigm for Social Sciences and Linguistics: Theoretical Basis and Perspectives

139

(Cangelosi and Parisi, 2002; Nettle, 1999; Kirby et al.,

2014). This is because of the lack of data about the

conditions and dynamics that caused the emergence

and ﬁrst stages of evolution of languages.

Both, the synchronous and the evolutionary state

of the language, need the knowledge of other aspects

which are mainly social, historical and cultural. This

fact has boosted the development of other branches

of linguistics form the perspective of complexity, like

sociolinguistics (Mufwene, 2013; Bastardas-Boada,

2013), dialectology (Massip-Bonet, 2018b) and his-

torical linguistics (Massip-Bonet, 2018a).

Languages are fundamentally dynamic phenom-

ena. The causes of its change and evolution, as well

as its state in a given time, are subject to different

interdependent pressures. In general, to explain the

processes that lead to the use or not of a language, or

to do it in certain contexts, the simulation of ecosys-

tems and adaptive systems is often used. Languages

adapt to contexts and environments, and their uses and

structures depend on a fragile balance, which is based

on numerous vectors. All this network of interdepen-

dences has been treated by Bastardas-Boada (2013)

and Ellis and Larsen-Freeman (2009), among others.

This ecological tension that models the dynam-

ics of sociolinguistics has been described in Terborg

(2006) and Terborg and Landa (2013). The authors in-

troduce the ‘Ecology of Pressure’s as a complex the-

ory that computes different pressures – brain/mind,

habits of social behaviour, demo-social groupings, so-

cioeconomic structure, the media and political power

– that constantly interact to determine which is the

force that prevails in our use of language in every sit-

uation.

5 CONCLUSIONS

In this paper we have considered complexity as a re-

search paradigm, both for sciences and humanities.

Emerging between the second and the third millen-

nium, this framework has been involved in what can

be called a methodological war, especially in social

sciences. This implies a controversy between quanti-

tative and qualitative methods, that cannot be avoided,

taking into account that this research proposal takes

the methods mainly from physics and mathematics,

but is, par excellence, multidisciplinary and ready to

develop MMR research designs.

For understanding why the concept complexity is

so important for social sciences, it is necessary to un-

derstand how complex systems are deﬁned, their fea-

tures and commonalities. We highlight throughout the

paper that natural language is a prototypical complex

system. However, assuming this entails serious epis-

temological and methodological positions that are not

always easy to hold and practice in the ﬁeld of hu-

manities.

Finally some examples of how complexity can be

applied in very different manners to linguistics have

been presented.

New paradigms like complexity have an impact

in the view of the world, in theoretical and compre-

hensive approaches of knowledge and in the scientiﬁc

methodology. In this latter aspect, new tools for ap-

prehension and treatment of the data are necessary,

that are able to make us understand the phenomena

from a completely new perspective. Therefore, this

could be a revolution in the treatment and interaction

with (language) data. This opens a new way for a

joint and uniﬁed explanation of complex phenomena

that may appear in entities belonging to different sci-

ences.

However, nowadays complexity is not ready to

provide an integrated and uniﬁed corpus of theory that

conﬁgures a unifying paradigm. It has to create yet an

own terminology, language, methodology and the in-

tegrated interpretation that we claim is characteristic

of it. Summing up, a theoretical framework for com-

plex entities has to be built yet, capable to clarify the

interpretation of science we propose and, moreover,

establish the foundations of a paradigm that can be a

very effective umbrella for several areas of research

in the next decades.

REFERENCES

Allen, T. and Hoekstra, T. (1992). Toward a Uniﬁed Ecol-

ogy. Columbia University Press, New York.

Andrasson, A. (2014). Language complexity: An insight

from complex-system theory. International Journal

of Language and Linguistics, 2(2):74–88.

Ashby, W. (1956). An Introduction to Cybernetics. Chap-

man & Hall, London.

Bar-Yam, Y. (2002). Encyclopedia of Life Support Systems,

chapter General Features of Complex Systems.

Barab

asi, A.-L. and Albert, R. (1999). Emergence of scal-

ing in random networks. Science, 285(15 October

1999):509–512.

Bastardas-Boada, A. (2013). Sociolinguistics: Towards a

Complex Ecological View, pages 15–34. Springer,

Berlin.

Bateson, G. (1972). Steps to an Ecology of Mind. Ballantine

Books, New York.

Beckner, C., Blythe, R., Bybee, J., Christiansen, M., Croft,

W., Ellis, N., Holland, J., Ke, J., Larsen-Freeman, D.,

and Shoeneman, T. (2009). Language is a complex

adaptive system. Language Learning, 59(Suppl. 1,

December 2009):1–26.

COMPLEXIS 2020 - 5th International Conference on Complexity, Future Information Systems and Risk

140

Bohm, D. (1980). Wholeness and the Implicate Order.

Routledge & Kegan, London.

Bryman, A. (1990). Quantity and Quality in Social Re-

search. Unwin Hyman, ﬁrst ed. 1988 edition.

Buchanan, D. and Bryman, A. (2007). Contextualizing

methods choice in organizational research. Organi-

zational Research Methods, 10(3):483–501.

Cangelosi, A. and Parisi, D. (2002). Computer Simulation:

A New Scientiﬁc Approach to the Study of Language

Evolution, pages 3–28. Springer London, London.

Creswell, J. (2003). Research design. Qualitative, quantita-

tive and mixed methods approaches. Sage, Thousand

Oaks, CA, ﬁrst edition 1994 edition.

Creswell, J. and Plano Clark, V. (2007). Designing and

conducting mixed methods research. Sage, Thousand

Oaks, CA.

Croft, W. (2000). Explaining language change: An evolu-

tionary approach. Longman, London.

Dahl, O. (2004). The Growth and Maintenance of Linguis-

tic Complexity. Number 71 in Studies in Language

Companion Series. John Benjamins, Amsterdam.

Denzin, N. and Lincoln, I. (2005). Handbook of Qual-

itative Research. Third Edition, chapter Competing

paradigms in qualitative research. Sage.

Ellis, N. and Larsen-Freeman, D. (2009). Language as a

Complex Adaptative System. Blackwell Publishing,

Oxford.

Fenk-Oczlon, G. and Fenk, A. (2008). Language complex-

ity: Typology, contact, change, chapter Complexity

trade-offs between the subsystems of language. John

Benjamins.

Gell-Mann, M. (1995). What is complexity? Complexity,

1(1):16–19.

Guba, E. (1990). The Paradigm Dialog. SAGE Publica-

tions.

Guba, E. and Lincoln, Y. (1988). Qualitative ap-

proaches to evaluation in education, chapter Do in-

quiry paradigms imply inquiry methodologies?, pages

89–115.

Guba, E. and Lincoln, Y. (1994). Handbook of Qualitative

Research, chapter Competing paradigms in qualitative

research. Sage.

Hawkins, J. (2004). Efﬁciency and Complexity in Gram-

mars. Oxford University Press, Oxford.

Holland, J. H. (2006). Studying complex adaptive systems.

Journal of Systems Science and Complexity, 19(1):1–

Hutchins, E. and Hazlehurst, B. (2002). Auto-organization

and Emergence of Shared Language Structure, pages

279–305. Springer London, London.

Johnson, R. and Onwuegbuzie, A. (2004). Mixed methods

research: A research paradigm whose time has come.

Educational Researcher, 33(7):14–26.

Kirby, S. (2002). Natural language from artiﬁcial life. Arti-

ﬁcial Life, 8(2):185–215.

Kirby, S., Grifﬁths, T., and Smith, K. (2014). Iterated learn-

ing and the evolution of language. Current Opinion in

Neurobiology, 28:108–114.

Kirby, S. and Hurford, J. R. (2002). The Emergence of Lin-

guistic Structure: An Overview of the Iterated Learn-

ing Model, pages 121–147. Springer London, London.

Kuhn, T. (1962). The Structure of Scientiﬁc Revolutions.

University of Chicago Press, Chicago, Il.

Marcus, S. (1974). Linguistics as a pilot science, pages

2871–2887. Mouton, The Hague.

Massip-Bonet, . (2013). Complexity Perspectives on Lan-

guage, Communication and Society, chapter Lan-

guages as Complex Adaptive Systems: Towards an

Integrative Linguistics, pages 35–60. Springer, Hei-

delberg.

Massip-Bonet, . (2018a). Imbricacions entre variaci

hist

orica i variaci

o geogr

aﬁca en la llengua cata-

lana. UBe Publicacions i Edicions de la Universitat

de Barcelona, Barcelona.

Massip-Bonet, . (2018b). L

exic i sem

antica dialectal:

aproximaci

o des de la compl

exica. UBe Publicacions

i Edicions de la Universitat de Barcelona, Barcelona.

Masterman, M. (1970). Criticism and the Growth of Knowl-

edge, chapter The Nature of a Paradigm, pages 59–89.

Cambridge University Press, Cambridge, UK.

Maturana, H. and Varela, F. (2004). De m

aquinas y seres

vivos. Autopoiesis: la organizaci

on de lo vivo. Lumen,

Buenos Aires.

Mertens, D. (2005). Research and Evaluation in Education

and Psychology: Integrating diversity with quantita-

tive, qualitative, and mixed methods. Sage, Boston,

Mass, 2nd edition.

Miestamo, M. (2008). Language complexity: Typology,

contact, change, chapter Grammatical complexity in

a cross-linguistic perspective. John Benjamins.

Miestamo, M., Sinnem

aki, K., and Karlsson, F. (2008).

Language complexity: Typology, contact, change.

Number 94 in Studies in Language Companion Series.

John Benjamins, Amsterdam.

Mingers, J. (2003). The paucity of multimethod research: a

review of the information systems literature. Informa-

tion Systems Journa, (13):233–249.

Morin, E. (1973). Le paradigme perdu: la nature humaine.

Editions du Seuil, Paris.

Morin, E. (2008). La m

ethode.

Editions du Seuil, Paris.

Mufwene, S. (2013). Complexity Perspectives on Lan-

guage, Communication and Society, chapter The

Emergence of Complexity in Language: An Evolu-

tionary Perspective, pages 197–218. Springer, Hei-

delberg.

Narrog, H. and Auwera, J. v. d. (2011). Complexity Per-

spectives on Language, Communication and Society,

chapter Grammaticalization and semantic maps, pages

318–327. Oxford University Press, Oxford.

Nettle, D. (1999). Using social impact theory to simulate

language change. Lingua, 108(2–3):95–117.

Neuman, W. (2006). Social Research Methods: Qualitative

and quantitative approaches. Pearson, Boston, Mass,

6th edition.

Ort

ı, A. (1995). La confrontaci

on de modelos y niveles

epistemol

ogicos en la g

enesis e historia de la inves-

tigaci

on social, pages 85–95. S

ıntesis, Madrid.

Complexity as a Paradigm for Social Sciences and Linguistics: Theoretical Basis and Perspectives

141

Popper, K. (1962). Conjectures and Refutations: The

Growth of Scientiﬁc Knowledge. Routledge, edition

2002 edition.

Prigogine, I. and Stengers, I. (1992). Entre le temps et

l’

eternit

e. Flammarion, Paris.

Serrano, A., Blanco, F., Ligero, J., Alvira, F., Esco-

bar, M., and S

aenz, A. (2009). La investigaci

multim

etodo. Technical report, Avalilable at

http://eprints.ucm.es/30034/1/araceli20serrano%

20articulacion metodologica. serrano blanco alvira.

pdf.

Smith, J. (1998). The Book. The publishing company, Lon-

don, 2nd edition.

Smith, J. (2011). Mixed methods research: The ﬁve ps

framework. The Electronic Journal of Business Re-

search Methods, 9(2):96–108.

Sol

e, R. (2009). Redes complejas. Del genoma a Internet.

Tusquets, Barcelona.

Tashakkori, A. and Teddlie, C. (2010). Handbook of Mixed

Methods in Social & Behavioral Research. Sage, Cal-

ifornia.

Terborg, R. (2006). La ‘ecolog

ıa de presiones’ en el de-

splazamiento de las lenguas ind

ıgenas por el espa

nol.

presentaci

on de un modelo. Forum: Qualitative So-

cial Research, 7(4).

Terborg, R. and Landa, L. G. (2013). Sociolinguistics: To-

wards a Complex Ecological View, pages 219–246.

Springer, Berlin.

Vemuri, V. (1978). Modeling of Complex Systems: An In-

troduction. Academic Press, New York.

von Bertalanffy, L. (1968). General Systems Theory: Foun-

dations, Development, Applications. George Braziller,

New York.

Wiener, N. (1990). Cybernetics or Control and Communi-

cation in the Animal and the Machine. The MIT Press,

Cambridge, Massachusetts.

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