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Systems Sensing: A Case for Embodied, Arts-Based Responses to Complex Problems

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Abstract

Many of the pressing problems of our times can be considered complex in a scientific sense. A complex system is one where there are multiple interacting elements which all interact at the same time and all influence each other (like the climate system for example). The deeply unpredictable nature of complex dynamics has become better understood in recent times, along with fresh attention for how we make decisions and process complex information. This chapter poses the question: To what extend do embodied, arts-based practices give us access to intuitive knowledge that is relevant in complex situations? The reader will be taken on a journey of exploration into a world of ‘systems sensing’ as opposed to ‘systems thinking’—a world where embodied methodologies support people through both their professional and their personal decision-making processes. One such methodology is InterPlay, an arts-based practice that works with improvised movement, voice, story, and connection. Drawing on her own experience as an InterPlay leader along with her training in complex systems science and her research into embodiment, Sophia will build a case as to how embodied ways of knowing can provide a powerful complement to the intellect when faced with a complex problem.

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Notes

  1. 1.

    For more on self-organisation and emergence see Johnson (2001), Kauffman (1995), Mitchell (2009), Strogatz (2003).

  2. 2.

    Whilst nonlinearity is a fundamental feature of complex systems, it is surprisingly hard to find good concise explanations of the concept. I can recommend Mitchell (2009, pp. 22–27) and Capra (1997, pp. 121–123) to give you a better feel for nonlinearity.

  3. 3.

    Introduction to Complexity MOOC (2015), video 6.3, www.complexityexplorer.org

  4. 4.

    This way of describing the two is very common, you can find it in Meyers (2002, p. 57) and Koch et al. (2013, p. 84.)

  5. 5.

    For example, when working on economic problems, scientists may model the ‘agents’ involved and allow them to interact in a computer simulation. Or when trying to understand population dynamics they may use iterative equations which model how population numbers evolve under certain conditions (each iteration represents one a generation, the population number at the end of a cycle is fed back into the equation and the equation is ‘iterated’ another time to get the population at the next generation).

  6. 6.

    https://www.presencing.org/#/aboutus/spt, accessed June 2018.

  7. 7.

    http://isca-network.org/foundations-of-systemic-constellations, accessed September 2018.

  8. 8.

    http://www.playbacktheatre.org, accessed September 2018.

  9. 9.

    http://scientific-theatre.org/about-us/our-approach, accessed September 2018.

  10. 10.

    www.interplay.org, accessed September 2018.

  11. 11.

    For more on working with affinity diagrams see Sarkissian and Hurford (2010).

  12. 12.

    www.presencing.org/#/news/news/the-origin-of-social-presencing-theatre, accessed June 2018.

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van Ruth, S. (2020). Systems Sensing: A Case for Embodied, Arts-Based Responses to Complex Problems. In: Bussey, M., Mozzini-Alister, C. (eds) Phenomenologies of Grace. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-40623-3_17

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