Cost-Aware Bayesian Optimization for Prototyping Interactive Devices
Abstract
Deciding which idea is worth prototyping is a central concern in iterative design. A prototype should be produced when the expected improvement is high and the cost is low. However, this is hard to decide, because costs can vary drastically: a simple parameter tweak may take seconds, while fabricating hardware consumes material and energy. Such asymmetries, can discourage a designer from exploring the design space. In this paper, we present an extension of cost-aware Bayesian optimization to account for diverse prototyping costs. The method builds on the power of Bayesian optimization and requires only a minimal modification to the acquisition function. The key idea is to use designer-estimated costs to guide sampling toward more cost-effective prototypes. In technical evaluations, the method achieved comparable utility to a cost-agnostic baseline while requiring only of the cost; under strict budgets, it outperformed the baseline threefold. A within-subjects study with 12 participants in a realistic joystick design task demonstrated similar benefits. These results show that accounting for prototyping costs can make Bayesian optimization more compatible with real-world design projects.
Cite
@article{arxiv.2602.01774,
title = {Cost-Aware Bayesian Optimization for Prototyping Interactive Devices},
author = {Thomas Langerak and Renate Zhang and Ziyuan Wang and Per Ola Kristensson and Antti Oulasvirta},
journal= {arXiv preprint arXiv:2602.01774},
year = {2026}
}