Efficient system-wide coordination in noisy environments.

Many natural and social systems display global organization and coordination without centralized control. The origin of this global coordination is a topic of great current interest. Here we investigate a density-classification task as a model system for coordination and information processing in decentralized systems. We show that sophisticated strategies, selected under idealized conditions, are not robust to environmental changes. We also demonstrate that a simple heuristic is able to successfully complete the classification task under a broad range of environmental conditions. Our findings hint at the possibility that complex networks and ecologically efficient rules coevolve over time.

[1]  M. Newman,et al.  Renormalization Group Analysis of the Small-World Network Model , 1999, cond-mat/9903357.

[2]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[3]  J Overbaugh,et al.  Lymphokines modulate the growth and survival of thymic tumor cells containing a novel feline leukemia virus/Notch2 variant. , 1999, Veterinary immunology and immunopathology.

[4]  D. Watts,et al.  Small Worlds: The Dynamics of Networks between Order and Randomness , 2001 .

[5]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[6]  Peter J. Richerson,et al.  Why does culture increase human adaptability , 1995 .

[7]  Julio M. Ottino,et al.  Complex networks , 2004, Encyclopedia of Big Data.

[8]  R. Kessin.  Dictyostelium: Evolution, Cell Biology, and the Development of Multicellularity , 2001 .

[9]  M Mitchell,et al.  The evolution of emergent computation. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[10]  C. Heyes,et al.  Social learning in animals : the roots of culture , 1996 .

[11]  H. Hinrichsen,et al.  Critical coarsening without surface tension: the universality class of the voter model. , 2001, Physical review letters.

[12]  M Lampl,et al.  Saltation and stasis: a model of human growth. , 1992, Science.

[13]  Mathieu S. Capcarrère,et al.  Necessary conditions for density classification by cellular automata. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Claudio Castellano,et al.  Incomplete ordering of the voter model on small-world networks , 2003 .

[15]  R. Kessin,et al.  Chemotactic sorting to cAMP in the multicellular stages of Dictyostelium development. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[16]  H E Stanley,et al.  Classes of small-world networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Jie Wu,et al.  Small Worlds: The Dynamics of Networks between Order and Randomness , 2003 .

[18]  P. Krapivsky,et al.  Coarsening and persistence in the voter model. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[19]  L. Amaral,et al.  Small-World Networks: Evidence for a Crossover Picture , 1999, cond-mat/9903108.

[20]  Melanie Mitchell,et al.  Evolving cellular automata to perform computations: mechanisms and impediments , 1994 .

[21]  Duncan J. Watts,et al.  Book Review: Small Worlds. The Dynamics of Networks Between Order and Randomness , 2000 .