Collaboratively improvising magic: an approach to managing participation in an on-line drama

  • Authors:

  • Adam Drozd;John Bowers;Steve Benford;Chris Greenhalgh;Mike Fraser

  • Affiliations:

  • The Mixed Reality Laboratory, The University of Nottingham, UK and Royal Institute of Technology, Stockholm, Sweden;The Mixed Reality Laboratory, The University of Nottingham, UK and Royal Institute of Technology, Stockholm, Sweden;The Mixed Reality Laboratory, The University of Nottingham, UK and Royal Institute of Technology, Stockholm, Sweden;The Mixed Reality Laboratory, The University of Nottingham, UK and Royal Institute of Technology, Stockholm, Sweden;The Mixed Reality Laboratory, The University of Nottingham, UK and Royal Institute of Technology, Stockholm, Sweden

  • Venue:
  • ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
  • Year:
  • 2001

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Abstract

We describe how a behind-the-scenes production crew managed participation in an on-line improvised dramatic performance in a shared virtual world that was broadcast to viewers. We introduce the approach of collaboratively improvising magic, where participants indirectly request interactions with objects through extended incantations, rather than manipulating them directly. Invisible stage-hands follow these participants around the world, monitoring their activities and granting requests when appropriate. We describe how this was realised in Avatar Farm, a two hour long improvised drama that involved four members of the public, seven actors and an extensive production crew. We discuss the provision of technical support within the MASSIVE-3 system to realise our approach. Empirical analysis of interaction in Avatar Farm illustrates some key issues. We see how participants weave accounts of technical problems into the narrative; how actors vary the pacing of the narrative to co-ordinate the timing of a local scene in relation to parallel scenes that are happening elsewhere; amongst other matters. We conclude with some general lessons from our approach for CSCW.