Cost-Aware Bayesian Optimization for Prototyping Interactive Devices

• URL: http://arxiv.org/abs/2602.01774v1
• Date: Mon, 02 Feb 2026 07:59:50 GMT
• Title: Cost-Aware Bayesian Optimization for Prototyping Interactive Devices
• Abstract summary: We present an extension of cost-aware Bayesian optimization to account for diverse prototyping costs.<n>The key idea is to use designer-estimated costs to guide sampling toward more cost-effective prototypes.
• Score: 43.5450702289719
• License: http://creativecommons.org/licenses/by/4.0/
• 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 ${\approx}70\%$ 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.

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