Cluster formation by allelomimesis in real-world complex adaptive systems.

Animal and human clusters are complex adaptive systems and many organize in cluster sizes s that obey the frequency distribution D (s) proportional to s(-tau). The exponent tau describes the relative abundance of the cluster sizes in a given system. Data analyses reveal that real-world clusters exhibit a broad spectrum of tau values, 0.7 (tuna fish schools) <or=tau<or=4.61 (T4 bacteriophage gene family sizes). Allelomimesis is proposed as an underlying mechanism for adaptation that explains the observed broad tau spectrum. Allelomimesis is the tendency of an individual to imitate the actions of others and two cluster systems have different tau values when their component agents display unequal degrees of allelomimetic tendencies. Cluster formation by allelomimesis is shown to be of three general types: namely, blind copying, information-use copying, and noncopying. Allelomimetic adaptation also reveals that the most stable cluster size is formed by three strongly allelomimetic individuals. Our finding is consistent with available field data taken from killer whales and marmots.

[1]  J. Altmann,et al.  Baboon Ecology: African Field Research , 1970 .

[2]  K. Tsuchida,et al.  Distribution of foundress group size in Ropalidia fasciata and R. plebeiana (Hymenoptera: Vespidae): an analysis using zero-truncated distribution models , 2002, Journal of Ethology.

[3]  Sergey V. Buldyrev,et al.  Scaling and universality in animate and inanimate systems , 1996 .

[4]  E Bonabeau,et al.  Scaling in animal group-size distributions. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Christopher Monterola,et al.  Allelomimesis as a generic clustering mechanism for interacting agents , 2003 .

[6]  M. Huynen,et al.  The frequency distribution of gene family sizes in complete genomes. , 1998, Molecular biology and evolution.

[7]  R. W. Baird,et al.  Ecological and social determinants of group size in transient killer whales , 1996 .

[8]  K. Armitage,et al.  Social enhancement of fitness in yellow-bellied marmots. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[9]  R. May Uses and Abuses of Mathematics in Biology , 2004, Science.

[10]  E. Bonabeau,et al.  Possible universality in the size distribution of fish schools. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[11]  L. Edelstein-Keshet,et al.  Complexity, pattern, and evolutionary trade-offs in animal aggregation. , 1999, Science.

[12]  H. Stanley,et al.  Modelling urban growth patterns , 1995, Nature.

[13]  Raoul Kopelman,et al.  Percolation and cluster distribution. I. Cluster multiple labeling technique and critical concentration algorithm , 1976 .

[14]  Tomasz Wyszomirski,et al.  Modelling the role of social behavior in the persistence of the alpine marmot Marmota marmota , 2003 .

[15]  E. Danchin,et al.  Conspecific copying: a general mechanism of social aggregation , 2003, Animal Behaviour.

[16]  Joakim Hjältén,et al.  Truncated power laws: a tool for understanding aggregation patterns in animals? , 2000 .

[17]  Sarah F. Tebbens,et al.  Upper-truncated Power Laws in Natural Systems , 2001 .

[18]  M. Milinski,et al.  Reputation helps solve the ‘tragedy of the commons’ , 2002, Nature.

[19]  Simone Tarquini,et al.  Power law olivine crystal size distributions in lithospheric mantle xenoliths , 2002 .

[20]  R. Axtell Zipf Distribution of U.S. Firm Sizes , 2001, Science.

[21]  D. Turcotte,et al.  Forest fires: An example of self-organized critical behavior , 1998, Science.

[22]  Paul G. Higgs The mimetic transition: a simulation study of the evolution of learning by imitation , 2000, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[23]  May Lim,et al.  Self-organized queuing and scale-free behavior in real escape panic , 2003, Proceedings of the National Academy of Sciences of the United States of America.