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Complexity, Development, and Evolution in Morphogenetic Collective Systems

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Evolution, Development and Complexity

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

Abstract

Many living and nonliving complex systems can be modeled and understood as collective systems made of heterogeneous components that self-organize and generate nontrivial morphological structures and behaviors. This chapter presents a brief overview of our recent effort that investigated various aspects of such morphogenetic collective systems. We first propose a theoretical classification scheme that distinguishes four complexity levels of morphogenetic collective systems based on the nature of their components and interactions. We conducted a series of computational experiments using a self-propelled particle swarm model to investigate the effects of (1) heterogeneity of components, (2) differentiation/re-differentiation of components, and (3) local information sharing among components, on the self-organization of a collective system. Results showed that (a) heterogeneity of components had a strong impact on the system’s structure and behavior; (b) dynamic differentiation/re-differentiation of components and local information sharing helped the system maintain spatially adjacent, coherent organization; (c) dynamic differentiation/re-differentiation contributed to the development of more diverse structures and behaviors; and (d) stochastic re-differentiation of components naturally realized a self-repair capability of self-organizing morphologies. We also explored evolutionary methods to design novel self-organizing patterns, using interactive evolutionary computation and spontaneous evolution within an artificial ecosystem. These self-organizing patterns were found to be remarkably robust against dimensional changes from 2D to 3D, although evolution worked efficiently only in 2D settings.

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Notes

  1. 1.

    We note that this assumption is much less obvious than the first one, and if we did not adopt it, we would obtain 3 × 2 = 6 different classes. In this chapter, we limit our focus on the four-level classification presented above.

  2. 2.

    For more evolved patterns, see the Swarm Chemistry website: http://bingweb.binghamton.edu/~sayama/SwarmChemistry/

  3. 3.

    https://www.youtube.com/user/ComplexSystem/videos

  4. 4.

    http://bingweb.binghamton.edu/~sayama/NSF-RI-MCS/

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Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. 1319152. The author thanks Benjamin James Bush, Shelley Dionne, Craig Laramee, David Sloan Wilson, and Chun Wong for their contributions to this project.

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Authors and Affiliations

  1. Center for Collective Dynamics of Complex Systems, Department of Systems Science and Industrial Engineering, Binghamton University, State University of New York, Binghamton, NY, USA

    Hiroki Sayama

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  1. Hiroki Sayama
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Correspondence to Hiroki Sayama .

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Editors and Affiliations

  1. Department of Physics, Worcester Polytechnic Institute, Worcester, MA, USA

    Georgi Yordanov Georgiev

  2. Acceleration Studies Foundation, Los Gatos, CA, USA

    John M. Smart

  3. Department of Biology, Institute of Zoology, Molecular Evolutionary Biology, University of Hamburg, Hamburg, Germany

    Claudio L. Flores Martinez

  4. Department of Life Sciences, Centre for Culture and Evolution, Brunel University London, Uxbridge, UK

    Michael E. Price

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Sayama, H. (2019). Complexity, Development, and Evolution in Morphogenetic Collective Systems. In: Georgiev, G., Smart, J., Flores Martinez, C., Price, M. (eds) Evolution, Development and Complexity. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-00075-2_11

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