The Future of Good Decisions project uses Live Action Role Play (LARP) as a prefigurative research method, creating immersive, improvisational spaces where participants embody characters to explore alternative futures.

This approach is especially valuable in government settings, where established norms and rigid constraints often limit imaginative thinking about new technologies and processes.

The FGD project is staging a series of LARPs to explore emergent value and design priorities for human-AI co-decision systems. We aim to use LARP in the next phase of the project to redesign a realistic government system based on these principles.

  • Working with LARP legends Chaos League (Italy), we are designing an online game playable by the general public. The game looks at one way that an AI-driven military process might be remodelled as a civic, participatory one – putting into focus the limits of participation as a democratisation strategy in relation to the exercise of state force, as well as raising questions about individual responsibility in multi-agent decision contexts.

  • What would it look like if a government algorithmic decision system was co-designed by a more-than-human cast of participants? That’s the question posed by the LARP currently in development, in collaboration with Serpentine Galleries (UK) and leading research LARP creative practitioner Ruth Catlow (Furtherfield, UK). Ruth’s practice includes a long experience in the use of creative methods for the social exploration of new technologies.

Using LARP in your research or curious about its potential for designing algorithmic systems? We’d love to hear from you - especially if you’re in government or civil society.

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‘METHEXIS PARK’
SYSTEM CO-DESIGN LARP

EXPRESSION OF INTEREST

Methexis Park is a live-action roleplay focused on the co-design of an automated system for granting permits to a ‘smart’ urban green space with ecological importance. Participants collaboratively explore technical, political, and legal issues within an emergent narrative shaped by their input.

Read more.