A Bayesian model of receptivity to the novelty of functional ideas

This study proposes a mathematical model for understanding designers’ acceptance of new functional ideas, a key element in innovation of meaning. By aligning the Function-Behavior-Structure (FBS) framework with Bayesian inference, we model the acceptance of novelty as predicted Bayesian Surprise (pBS), representing the expected epistemic value of implementing a new idea. An experiment was conducted to test the hypothesis that functional ideas with higher prior variance are more likely to be accepted despite higher prediction error. Overall results showed decreased acceptance with greater prediction error. Participants with high specific curiosity were more inclined to accept ideas with higher prior variance. These findings suggest that differences in prior variance of ideas influence how novelty is received in design, and that pBS-based modeling may offer insights into idea evaluation strategies.