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Modeling Automation Trust Diffusion in Teams: Linking Micro- and Macro-Trust Dynamics
1Department of Industrial & Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA.
Human Factors
|August 14, 2026
Summary
Team trust in automation may diverge, not converge, over time. A new dynamic model shows trust levels can vary between team members, especially in long-duration missions, requiring new calibration strategies.
Area of Science:
- Human-Computer Interaction
- Team Dynamics
- Automation Trust
Background:
- Existing trust models assume convergence over short durations.
- Long-duration missions may exhibit contingent trust evolution.
- Team interactions significantly influence automation trust.
Purpose of the Study:
- To develop a dynamic model of trust evolution in teams.
- To assess convergence versus divergence of trust over time.
- To incorporate team interactions into trust dynamics.
Main Methods:
- Stochastic, discrete-event agent-based modeling.
- Inclusion of team conversations and turn-taking.
- Integration of individual automation experience.
Main Results:
- Model demonstrated divergent trust behavior.
- Team members' trust levels did not converge.
- Findings align with human subjects data.
Conclusions:
- Dynamical models can exhibit contingent trust behavior.
- Trust in automation can diverge within and across teams.
- Trust calibration must account for potential divergence.
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