A Computational Model of Commitment Under Uncertainty in Collaborative Design (Version 1.0)

Industrial production transformed society by enabling the efficient manufacture of standardised goods at unprecedented scale. While this model achieved remarkable gains in productivity and affordability, it also relied upon forms of coordination that privileged standardisation over individual variation, concentrated decision-making within relatively centralised production systems, and reduced opportunities for ongoing participation by designers, makers, and end users once production decisions had been made.

Advances in artificial intelligence, digital communication, and computational reasoning invite reconsideration of these assumptions. Rather than using computation solely to optimise existing production pipelines, emerging technologies make it increasingly feasible to support personalised collaboration at scales that were previously impractical. This shift raises a fundamental question: how can computational systems support distributed human agency while assisting groups in determining when commitment to a course of action is justified?

This paper presents a computational model of collaborative commitment under uncertainty. Rather than modelling collaboration as communication alone or seeking consensus as an end in itself, the model represents collaboration as an evolving process of shared reasoning in which participants progressively construct, revise, and align mental models before committing finite resources. Commitment is evaluated through the interaction of three computational constructs—confidence, consequentiality, and adjustability—allowing uncertainty to be managed rather than eliminated.

The model has been implemented and evaluated within a simulation environment developed through the FORME collaborative design platform. Although motivated by bespoke fashion design, the computational framework is intentionally domain-independent and is applicable to collaborative decision-making wherever uncertainty, shared interpretation, and resource commitment intersect.