New Paper on Personalized Trust Prediction Published in IEEE THMS
June 24, 2026
The increasing adoption of autonomous systems has highlighted the need to understand and predict users' trust
in these technologies. Existing trust prediction models perform well for most users but fail to capture the volatile,
highly fluctuating trust patterns of “oscillators.” This raises two key challenges: First, developing a model that can
accurately approximate oscillators' dynamics, and second, identifying which individuals are likely to be oscillators so
the model can be selectively applied.
To address the first challenge, we propose a discounting trust model that prioritizes
recent interactions while attenuating older ones, improving prediction accuracy for oscillators
(optimal discount factor lambda=0.8). To address the second, we develop a classification model
based on seven personal characteristics to screen for potential oscillators. Finally, we integrate the
two approaches, applying the discounting trust model only to individuals predicted as oscillators.
We used two datasets (N = 130 development, N = 41 validation). The development dataset was used to
identify the optimal discount factor and train the screening classifier. The validation dataset was used
to identify potential oscillators and to validate the performance of the discounting trust model on them.
Four out of the 41 participants were identified as potential oscillators. We compared the discounting
and non-discounting baseline models using a linear mixed-effects model that accounted for autocorrelation.
The discounting model significantly reduced prediction errors compared to the baseline model (p<.001).
The findings showcase the value of incorporating both personal traits and a discount factor to enhance trust
prediction for oscillators.
Citation: Chung, H., Bhat, S. & Yang, X. J. (2026). Personalized Trust Prediction in Human-Autonomy Interaction: A Discounting Model.
IEEE Transactions on Human-Machine Systems.