Skip to main content

Emergent Patterns in Dance Improvisation and Choreography

  • Conference paper
Unifying Themes in Complex Systems IV

Abstract

In a traditional choreography a choreographer determines the motions of a dancer or a group of dancers. Information theory shows that there is a limit to the complexity that can be created in any given amount of time. This is true even when building on previous work, since movements and their interactions have to be communicated to the dancers. When creating a group work, choreographers circumvent this problem by focusing either on the movements of individual dancers (giving rise to intricate movements but within a simple spatiotemporal organization) or on the overall structure (intricate patterns but simple movements) or by creating room for the dancers to fill in part of the movements. Complexity theory offers a different paradigm towards the generation of enticing patterns. Flocks of birds or schools of fish for instance are considered ‘beautiful’ but lack a central governing agent. Computer simulations show that a few simple rules can give rise to the emergence of the kind of patterns seen in flocks or swarms. In these models individual agents are represented by dots or equivalent shapes. To be of use to choreography and to be implemented on or rather with dancers, some additional rules will therefore have to be introduced. A number of possible rules are presented, which were extracted from ‘real life’ experiments with dancers. The current framework for modeling flocking behavior, based on local interactions between single agents, will be extended to include more general forms of interaction. Dancers may for instance perceive the global structure they form, e.g. a line or a cluster, and then put that knowledge to creative use according to some pre-established rules, e.g. if there is a line, form a circle or if there is a cluster spread out in all directions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliography

  1. Adami, C., & Cerf, N.J. 2000, Physical complexity of symbolic sequences. Physica D 137, 62–69.

    Article  MATH  ADS  MathSciNet  Google Scholar 

  2. Birkhoff, G.D., 1956, Mathematics of Aesthetics, in The World of Mathematics Vol. 4 edited by J.R. Newman, Simon and Schuster (New York), 2185–2195.

    Google Scholar 

  3. Birkhoff, G.D., 1933, Aesthetic Measure, Harvard University Press (Cambridge, MA).

    MATH  Google Scholar 

  4. Bonabeau, E., Dorigo, M., & Theraulaz, G., 2000, Inspiration for optimization from social insect behaviour, Nature 406, 39–42.

    Article  ADS  Google Scholar 

  5. Bonabeau, E., Dorigo, M., & Theraulaz, G., 1999, Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press (Oxford).

    MATH  Google Scholar 

  6. Csahók, Z., & Vicsek, T., 1995, Lattice gas model for collective biological motion, Physical Review E 52, 5297.

    Article  ADS  Google Scholar 

  7. Cszikszentmihalyi, M., 1992, Flow. The Psychology of Happines, Rider (London).

    Google Scholar 

  8. Czirok, A., Stanley, H.E., & Vicsek, T., 1997, Spontaneously ordered motion of self-propelled particles, Journal of Physics A 30, 1375.

    Article  Google Scholar 

  9. Gell-Mann, M., 1994, The Quark and the Jaguar. Adventures in the Simple and Complex, Abacus (London).

    MATH  Google Scholar 

  10. Helbing, D., & Molnár, P., 1995, Social force model for pedestrian dynamics, Physical Review E 51, 4282–4286.

    Article  ADS  Google Scholar 

  11. Helbing, D., 2001, Traffic and related self-driven many-particle systems, Reviews of Modern Physics 73, 1067–1141.

    Article  ADS  Google Scholar 

  12. Helbing, D., Farkas, I., & Vicsek, T., 2000, Simulating dynamical features of escape panic, Nature 407, 487–490.

    Article  ADS  Google Scholar 

  13. Holland, J.H., 1995, Hidden Order. How adaptation builds complexity, Helix Books (Reading, MA).

    Google Scholar 

  14. Holland, J.H., 1998, Emergence. From chaos to order, Perseus Books (Reading, MA).

    MATH  Google Scholar 

  15. Koshelev, M., 1998, Towards The Use of Aesthetics in Decision Making: Kolmogorov Complexity Formalizes Birkhoff’s Idea, Bulletin of the European Association for Theoretical Computer Science, 66, 166–170.

    MATH  MathSciNet  Google Scholar 

  16. Kreinovich, V., Longpre, L., & Koshelev, M., 1998, Kolmogorov complexity, statistical regularization of inverse problems, and Birkhoff’s formalization of beauty, in Bayesian Inference for Inverse Problems, Proceedings of the SPIE/International Society for Optical Engineering edited by A. Mohamad-Djafari, 3459, 159–170.

    Google Scholar 

  17. Li, M., & Vitanyi, P.M.B., 1997, An introduction to Kolmogorov complexity and its applications 2nd edition, Springer (New York).

    MATH  Google Scholar 

  18. Néda, Z., Ravasz, E., Brechet, Y., Vicsek, T., & Barabasi, A.-L., 2000, The sound of many hands clapping, Nature 403, 849–850.

    Article  ADS  Google Scholar 

  19. Ramachandran, V.S., & Hirstein, W., 1999, The science of art. A neurological theory of aesthetic experience, Journal of Consciousness Studies 6,6–7, 15–51.

    Google Scholar 

  20. Reynolds, C.W., 1987, Flocks, herds, and schools: A distributed behavioral model, Computer Graphics 21,4, 25–34.

    Article  MathSciNet  Google Scholar 

  21. Rietstap, I. 2002, Danstaal Forsythe te complex voor publiek [transl.: Dance language Forsythe too complex for audience], NRC Handelsblad, 15 April.

    Google Scholar 

  22. Toner, J., & Tu, Y., 1998 Flocks, herds and schools: a quantitative theory of flocking, Physical Review E 58, 4828–4858.

    Article  ADS  MathSciNet  Google Scholar 

  23. Wolpert, D.H., & Turner, K., 1999, An introduction to collective intelligence, in Handbook of Agent Technology edited by J.M. Bradshaw, AAAI Press/MIT Press (Cambridge, MA).

    Google Scholar 

  24. Zeki, S., 1999, Inner vision. An exploration of art and the brain, Oxford University Press (Oxford).

    Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 NECSI Cambridge, Massachusetts

About this paper

Cite this paper

Hagendoorn, I. (2008). Emergent Patterns in Dance Improvisation and Choreography. In: Minai, A.A., Bar-Yam, Y. (eds) Unifying Themes in Complex Systems IV. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73849-7_21

Download citation

Publish with us

Policies and ethics