Naming Game and Computational Modelling of Language Evolution
Institute of Linguistics, Adam Mickiewicz University
60-371 Poznań, Poland
e-mail: lipowska@amu.edu.pl
Received:
Received: 20 September 2010; revised: 24 November 2010; accepted: 9 February 2011; published online: 7 March 2011
DOI: DOI:10.12921/cmst.2011.17.01.41-51
OAI: oai:lib.psnc.pl:738
Abstract:
Computational modelling with multi-agent systems has become an important technique in studying language evolution. We present a brief introduction into this rapidly developing field, as well as our own contributions, which include an analysis of the evolutionary naming game model. In this model, communicating agents, which try to establish a common vocabulary, are equipped with an evolutionarily selected learning ability. Such a coupling of biological and linguistic ingredients results in an abrupt transition: upon a small change of the model control parameter, a poorly communicating group of agents with small learning abilities transforms into almost perfectly communicating group of agents with large learning abilities. Genetic imprinting of the learning abilities progresses through the Baldwin effect: initially linguistically unskilled agents learn a language, which creates a niche where there is an evolutionary pressure for the increase of learning ability. Under the assumption that communication intensity increases continuously with finite speed, the transition is split into several transition-like changes. It shows that the speed of cultural changes, that sets an additional characteristic time scale, might be yet another factor affecting the evolution of language. In our opinion, this model shows that linguistic and biological processes have a strong influence on each other and this influence certainly has contributed to an explosive development of our species.
Key words:
agent systems, Baldwin effect, bio-linguistics, computer modelling, naming game
References:
[1] N. Chomsky, Aspects of the Theory of Syntax. MIT Press,
Cambridge 1965.
[2] G. Sampson, Educating Eve: The ’Language Instinct’ Debate.
Cassell, London 1997.
[3] N. Chomsky, Language and Mind, Harcourt Brace Jovanovich.
San Diego 1972.
[4] S. Pinker, P. Bloom, Natural language and natural selection.
Behavioral and Brain Sciences 13 (4), 707-784 (1990).
[5] R.S. Jackendoff, Languages of the Mind. MIT Press, Cambridge
1992.
[6] C. Knight et al. (eds.), The Evolutionary Emergence of Language:
Social Function and the Origin of Linguistic Form.
Cambridge University Press, Cambridge 2000.
[7] M.A. Nowak, N.L. Komarova, Towards an evolutionary
theory of language. Trends in Cognitive Sciences 5 (7),
288-295 (2001). M.A. Nowak, D.C. Krakauer, The evolution
of language. Proc. Natl. Acad. Sci. USA 96 (14), 8028-
8033 (1999).
[8] K. Smith, The Transmission of Language: models of biological
and cultural evolution. Ph.D. thesis, The University
of Edinburgh, Edinburgh 2003.
[9] S.J. Gould, The limits of adaptation: Is language a spandrel
of the human brain? Paper presented to the Cognitive
Science Seminar, Center for Cognitive Science, MIT 1987.
[10] S.J. Gould, R.C. Lewontin, The spandrels of San Marco
and the Panglossian paradigm: a critique of the adaptationist
programme. Proc. R. Soc. Lond. B 205 (1161), 581-
598 (1979).
[11] J. Maynard Smith, E. Szathmáry, Major Transitions in Evolution.
Freeman, Oxford 1995.
[12] S. Számadó, E. Szathmáry, Selective scenarios for the
emergence of natural language. Trends in Ecology & Evolution
21(10), 555-561 (2006).
[13] W.D. Hamilton, The genetical evolution of social behaviour
I and II. Journal of Theoretical Biology 7, 1-16, 17-52
(1964).
[14] R.L. Trivers, The evolution of reciprocal altruism. Quarterly
Review of Biology 46, 35-57 (1971).
[15] J.-L. Dessalles, Altruism, status, and the origin of relevance.
In: J.R. Hurford et al. (eds.), Approaches to the
Evolution of Language: Social and Cognitive Bases.
Cambridge University Press, Cambridge, 130-147 (1998).
[16] J. Noble, Cooperation, competition and the evolution of
prelinguistic communication. In: Knight et al. (eds.) Evolutionary
Emergence of Language. Cambridge University
Press, Cambridge, 40-61 (2000).
[17] J.M. Baldwin, A new factor in evolution. American Naturalist
30, 441-451, 536-553 (1896).
[18] G.G. Simpson, The Baldwin effect. Evolution 7, 110-117
(1953).
[19] B.H. Weber, D.J. Depew (eds.), Evolution and Learning –
The Baldwin Effect Reconsidered. MIT Press, Cambridge
2003.
[20] P.D. Turney, Myths and legends of the Baldwin effect. In:
T. Fogarty, G. Venturini (eds.) Proceedings of the ICML-96
(13th International Conference on Machine Learning, Bari,
Italy), 135-142 (1996).
[21] H. Yamauchi, Baldwinian Accounts of Language Evolution.
Ph.D. thesis, The University of Edinburgh, Edinburgh 2004.
[22] M.H. Christiansen, N. Chater, Language as shaped by the
brain. Behavioral and Brain Sciences 31, 489-558 (2008).
[23] M.A. Nowak, N.L. Komarova, P. Niyogi, Computational
and evolutionary aspects of language. Nature 417, 611-617
(2002).
[24] B. de Boer, Computer modelling as a tool for understanding
language evolution. In: Gonthier et al. (eds.) Evolutionary
Epistemology, Language and Culture – A non-adaptationist,
systems theoretical approach. Springer, Dordrecht,
381-406 (2006).
[25] S. Kirby, Natural language from Artificial Life. Artificial
Life 8 (2), 185-215 (2002).
[26] L. Steels, Iterated Learning versus Language Games. Two
models for cultural language evolution. In: C. Hemelrijk,
E. Bonabeau (eds.) Proceedings of the International Workshop
of the Self-Organization and Evolution of Social
Behaviour. University of Zurich, Switzerland, Zurich 2002.
[27] S. Kirby, J. Hurford, The emergence of linguistic structure;
An overview of the Iterated Learning Model. In: A. Canelosi,
D. Parisi (eds.) Simulating the Evolution of Language.
Springer-Verlag, Berlin p. 121-148, 2001.
[28] H. Brighton, Compositional syntax from cultural transmission.
Artificial Life 8 (1), 25-54 (2002).
[29] L. Steels, A self-organizing spatial vocabulary. Artificial
Life 2 (3), 319-332 (1995).
[30] A. Lipowski, D. Lipowska, Bio-linguistic transition and the
Baldwin effect in the evolutionary naming game model.
International Journal of Modern Physics C, 19 (3), 399-407
(2008).
[31] A. Baronchelli, M. Felici, V. Loreto, E. Caglioti, L. Steels,
Sharp transition towards shared vocabularies in multiagent
systems. Journal of Statistical Mechanics 06, P06014
(2006).
[32] L. Dall’Asta, A. Baronchelli, A. Barrat, V. Loreto, Nonequilibrium
dynamics of language games on complex networks.
Physical Review E 74, 036105 (2006).
[33] D. Nettle, Using social impact theory to simulate language
change. Lingua 108, 95-117 (1999). D. Nettle, Is the rate of
linguistic change constant? Lingua 108, 119-136 (1999).
[34] G. Hinton, S. Nowlan, How learning can guide evolution.
Complex Systems 1, 495-502 (1987).
[35] S. Munroe, A. Cangelosi, Learning and the evolution of
language: The role of cultural variation and learning costs in
the Baldwin effect. Artificial Life 8 (4), 311-339 (2002).
[36] C. Holden, The origin of speech. Science 303, 1316-1319
(2004).
[37] D. Abrams, S.H. Strogatz, Modelling the dynamics of language
death. Nature 424, 900 (2003).
[38] C. Schulze, D. Stauffer, S. Wichmann, Birth, survival and
death of languages by Monte Carlo simulation. Communications
in Computational Physics 3 (2), 271-294 (2008).
[39] P.M.C. de Oliveira, D. Stauffer, S. Wichmann, S.M. de Oliveira,
A computer simulation of language families. Journal of
Linguistics 44 (3), 659-675 (2008).
Computational modelling with multi-agent systems has become an important technique in studying language evolution. We present a brief introduction into this rapidly developing field, as well as our own contributions, which include an analysis of the evolutionary naming game model. In this model, communicating agents, which try to establish a common vocabulary, are equipped with an evolutionarily selected learning ability. Such a coupling of biological and linguistic ingredients results in an abrupt transition: upon a small change of the model control parameter, a poorly communicating group of agents with small learning abilities transforms into almost perfectly communicating group of agents with large learning abilities. Genetic imprinting of the learning abilities progresses through the Baldwin effect: initially linguistically unskilled agents learn a language, which creates a niche where there is an evolutionary pressure for the increase of learning ability. Under the assumption that communication intensity increases continuously with finite speed, the transition is split into several transition-like changes. It shows that the speed of cultural changes, that sets an additional characteristic time scale, might be yet another factor affecting the evolution of language. In our opinion, this model shows that linguistic and biological processes have a strong influence on each other and this influence certainly has contributed to an explosive development of our species.
Key words:
agent systems, Baldwin effect, bio-linguistics, computer modelling, naming game
References:
[1] N. Chomsky, Aspects of the Theory of Syntax. MIT Press,
Cambridge 1965.
[2] G. Sampson, Educating Eve: The ’Language Instinct’ Debate.
Cassell, London 1997.
[3] N. Chomsky, Language and Mind, Harcourt Brace Jovanovich.
San Diego 1972.
[4] S. Pinker, P. Bloom, Natural language and natural selection.
Behavioral and Brain Sciences 13 (4), 707-784 (1990).
[5] R.S. Jackendoff, Languages of the Mind. MIT Press, Cambridge
1992.
[6] C. Knight et al. (eds.), The Evolutionary Emergence of Language:
Social Function and the Origin of Linguistic Form.
Cambridge University Press, Cambridge 2000.
[7] M.A. Nowak, N.L. Komarova, Towards an evolutionary
theory of language. Trends in Cognitive Sciences 5 (7),
288-295 (2001). M.A. Nowak, D.C. Krakauer, The evolution
of language. Proc. Natl. Acad. Sci. USA 96 (14), 8028-
8033 (1999).
[8] K. Smith, The Transmission of Language: models of biological
and cultural evolution. Ph.D. thesis, The University
of Edinburgh, Edinburgh 2003.
[9] S.J. Gould, The limits of adaptation: Is language a spandrel
of the human brain? Paper presented to the Cognitive
Science Seminar, Center for Cognitive Science, MIT 1987.
[10] S.J. Gould, R.C. Lewontin, The spandrels of San Marco
and the Panglossian paradigm: a critique of the adaptationist
programme. Proc. R. Soc. Lond. B 205 (1161), 581-
598 (1979).
[11] J. Maynard Smith, E. Szathmáry, Major Transitions in Evolution.
Freeman, Oxford 1995.
[12] S. Számadó, E. Szathmáry, Selective scenarios for the
emergence of natural language. Trends in Ecology & Evolution
21(10), 555-561 (2006).
[13] W.D. Hamilton, The genetical evolution of social behaviour
I and II. Journal of Theoretical Biology 7, 1-16, 17-52
(1964).
[14] R.L. Trivers, The evolution of reciprocal altruism. Quarterly
Review of Biology 46, 35-57 (1971).
[15] J.-L. Dessalles, Altruism, status, and the origin of relevance.
In: J.R. Hurford et al. (eds.), Approaches to the
Evolution of Language: Social and Cognitive Bases.
Cambridge University Press, Cambridge, 130-147 (1998).
[16] J. Noble, Cooperation, competition and the evolution of
prelinguistic communication. In: Knight et al. (eds.) Evolutionary
Emergence of Language. Cambridge University
Press, Cambridge, 40-61 (2000).
[17] J.M. Baldwin, A new factor in evolution. American Naturalist
30, 441-451, 536-553 (1896).
[18] G.G. Simpson, The Baldwin effect. Evolution 7, 110-117
(1953).
[19] B.H. Weber, D.J. Depew (eds.), Evolution and Learning –
The Baldwin Effect Reconsidered. MIT Press, Cambridge
2003.
[20] P.D. Turney, Myths and legends of the Baldwin effect. In:
T. Fogarty, G. Venturini (eds.) Proceedings of the ICML-96
(13th International Conference on Machine Learning, Bari,
Italy), 135-142 (1996).
[21] H. Yamauchi, Baldwinian Accounts of Language Evolution.
Ph.D. thesis, The University of Edinburgh, Edinburgh 2004.
[22] M.H. Christiansen, N. Chater, Language as shaped by the
brain. Behavioral and Brain Sciences 31, 489-558 (2008).
[23] M.A. Nowak, N.L. Komarova, P. Niyogi, Computational
and evolutionary aspects of language. Nature 417, 611-617
(2002).
[24] B. de Boer, Computer modelling as a tool for understanding
language evolution. In: Gonthier et al. (eds.) Evolutionary
Epistemology, Language and Culture – A non-adaptationist,
systems theoretical approach. Springer, Dordrecht,
381-406 (2006).
[25] S. Kirby, Natural language from Artificial Life. Artificial
Life 8 (2), 185-215 (2002).
[26] L. Steels, Iterated Learning versus Language Games. Two
models for cultural language evolution. In: C. Hemelrijk,
E. Bonabeau (eds.) Proceedings of the International Workshop
of the Self-Organization and Evolution of Social
Behaviour. University of Zurich, Switzerland, Zurich 2002.
[27] S. Kirby, J. Hurford, The emergence of linguistic structure;
An overview of the Iterated Learning Model. In: A. Canelosi,
D. Parisi (eds.) Simulating the Evolution of Language.
Springer-Verlag, Berlin p. 121-148, 2001.
[28] H. Brighton, Compositional syntax from cultural transmission.
Artificial Life 8 (1), 25-54 (2002).
[29] L. Steels, A self-organizing spatial vocabulary. Artificial
Life 2 (3), 319-332 (1995).
[30] A. Lipowski, D. Lipowska, Bio-linguistic transition and the
Baldwin effect in the evolutionary naming game model.
International Journal of Modern Physics C, 19 (3), 399-407
(2008).
[31] A. Baronchelli, M. Felici, V. Loreto, E. Caglioti, L. Steels,
Sharp transition towards shared vocabularies in multiagent
systems. Journal of Statistical Mechanics 06, P06014
(2006).
[32] L. Dall’Asta, A. Baronchelli, A. Barrat, V. Loreto, Nonequilibrium
dynamics of language games on complex networks.
Physical Review E 74, 036105 (2006).
[33] D. Nettle, Using social impact theory to simulate language
change. Lingua 108, 95-117 (1999). D. Nettle, Is the rate of
linguistic change constant? Lingua 108, 119-136 (1999).
[34] G. Hinton, S. Nowlan, How learning can guide evolution.
Complex Systems 1, 495-502 (1987).
[35] S. Munroe, A. Cangelosi, Learning and the evolution of
language: The role of cultural variation and learning costs in
the Baldwin effect. Artificial Life 8 (4), 311-339 (2002).
[36] C. Holden, The origin of speech. Science 303, 1316-1319
(2004).
[37] D. Abrams, S.H. Strogatz, Modelling the dynamics of language
death. Nature 424, 900 (2003).
[38] C. Schulze, D. Stauffer, S. Wichmann, Birth, survival and
death of languages by Monte Carlo simulation. Communications
in Computational Physics 3 (2), 271-294 (2008).
[39] P.M.C. de Oliveira, D. Stauffer, S. Wichmann, S.M. de Oliveira,
A computer simulation of language families. Journal of
Linguistics 44 (3), 659-675 (2008).
