Microscopic and macroscopic simulation of competition between languages

Abstract The similarity of the evolution of human languages (or alphabets, bird songs, …) to biological evolution of species is utilized to study with up to 10 9 people the rise and fall of languages either by macroscopic differential equations similar to biological Lotka–Volterra equation, or by microscopic Monte Carlo simulations of bit-strings incorporating the birth, maturity, and death of every individual. For our bit-string model, depending on parameters either one language comprises the majority of speakers (dominance), or the population splits into many languages having in order of magnitude the same number of speakers (fragmentation); in the latter case the size distribution is log-normal, with upward deviations for small sizes, just as in reality for human languages. On a lattice two different dominating languages can coexist in neighbouring regions, without being favoured or disfavoured by different status. We deal with modifications and competition for existing languages, not with the evolution or learning of one language.

[1]  Language loss in Guatemala: A statistical analysis of the 1994 population census , 2005 .

[2]  D. Nettle,et al.  Linguistic diversity of the Americas can be reconciled with a recent colonization. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[3]  C. Schulze.  Potts-Like Model For Ghetto Formation In Multi-Cultural Societies , 2004, cond-mat/0409679.

[4]  Natalia L Komarova,et al.  Replicator-mutator equation, universality property and population dynamics of learning. , 2004, Journal of theoretical biology.

[5]  Gregory Bryan.  Computing in Science and Engineering , 1999, IEEE Software.

[6]  Dirk Jacobmeier.  Multidimensional Consensus Model on a BARABÁSI-ALBERT Network , 2004, cond-mat/0411350.

[7]  D. Stauffer,et al.  Simple bit-string model for lineage branching. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  R. Axelrod.  The Dissemination of Culture , 1997 .

[9]  Robert M. Westervelt,et al.  Nonlinear dynamics and chaos in extrinsic photoconductors , 1986 .

[10]  Dietrich Stauffer,et al.  MONTE CARLO SIMULATION OF THE RISE AND THE FALL OF LANGUAGES , 2005 .

[11]  Thomas C. Schelling,et al.  Dynamic models of segregation , 1971 .

[12]  Martin A Nowak,et al.  Chaos and language , 2004, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[13]  D. Stauffer,et al.  Evolutionary ecologyin silico: Does mathematical modelling help in understanding ‘generic’ trends? , 2005, Journal of Biosciences.

[14]  Andrzej Pekalski,et al.  A short guide to predator-prey lattice models , 2004, Computing in Science & Engineering.

[15]  Partha Niyogi,et al.  Evolutionary Consequences of Language Learning , 1997 .

[16]  H. Meyer-Ortmanns.  IMMIGRATION, INTEGRATION AND GHETTO FORMATION , 2002, cond-mat/0209242.

[17]  M A Nowak,et al.  Evolution of universal grammar. , 2001, Science.

[18]  김혜숙,et al.  Sociolinguistics , 2004, Language Teaching.

[19]  David Glenn Smith,et al.  Examining the Farming/Language Dispersal Hypothesis. , 2005 .

[20]  M. Pagel.  The Evolutionary Emergence of Language: The History, Rate and Pattern of World Linguistic Evolution , 2000 .

[21]  M. Eigen.  Selforganization of matter and the evolution of biological macromolecules , 1971, Naturwissenschaften.

[22]  A. I.,et al.  Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks , 2023, Biology.

[23]  David Gil,et al.  The World Atlas of Language Structures , 2005 .

[24]  M A Nowak,et al.  The evolution of language. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Dimitar Kazakov,et al.  COOPERATIVE NAVIGATION AND THE FACULTY OF LANGUAGE , 2004, Appl. Artif. Intell..

[26]  Ted Briscoe.  Grammatical acquisition: Inductive bias and coevolution of language and the language acquisition device , 2000 .

[27]  William J. Sutherland,et al.  Parallel extinction risk and global distribution of languages and species , 2003, Nature.

[28]  Zhao Rong-hui.  Linguistics and Philosophy:History and Sources , 2002 .

[29]  S. Solomon,et al.  The importance of being discrete: life always wins on the surface. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[30]  Xie Hong-kun,et al.  Nature of Science , 2002 .

[31]  D. Nettle.  Using Social Impact Theory to simulate language change , 1999 .

[32]  Thadeu J.P. Penna,et al.  A bit-string model for biological aging , 1995, cond-mat/9503099.

[33]  S. Redner.  A guide to first-passage processes , 2001 .

[34]  P. Niyogi,et al.  Computational and evolutionary aspects of language , 2002, Nature.

[35]  S. Strogatz,et al.  Linguistics: Modelling the dynamics of language death , 2003, Nature.

[36]  P. Drozd,et al.  The size distribution of conspecific populations: the peoples of New Guinea , 2000, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[37]  Junfu Zhang,et al.  A DYNAMIC MODEL OF RESIDENTIAL SEGREGATION , 2004 .

[38]  Ambarish Kunwar.  EVOLUTION OF SPATIALLY INHOMOGENEOUS ECOSYSTEMS: AN UNIFIED MODEL BASED APPROACH , 2004 .

[39]  Angelo Cangelosi,et al.  Simulating the Evolution of Language , 2002, Springer London.

[40]  Teemu Leppänen,et al.  Modeling language competition , 2004 .