Modeling Automation Trust Diffusion in Teams: Linking Micro- and Macro-Trust Dynamics

ObjectiveWe develop a dynamic model to understand how trust in automation evolves within teams through team member interactions, assessing whether trust converges or diverges over time.BackgroundExisting research assumes that individual and team trust in automation converges over time, but these studies typically last only hours or days. This assumption may not hold for long-duration missions, like deep space exploration, where team dynamics and trust might evolve in a contingent rather than convergent manner. Contingent behavior evolves towards different endpoints based on small perturbations, whereas convergent behavior evolves to similar endpoints.MethodsWe developed a stochastic, discrete-event agent-based trust dynamics model that goes beyond existing models that only consider past interactions with automation. Our model incorporates team conversations, individual automation experience, and turn-taking interactions.ResultsConsistent with human subjects data, the model showed divergent trust behavior where team members' trust levels did not converge to similar values over time.ConclusionsDynamical models of trust in teams can show contingent behavior. Trust in automation within and across teams can diverge, indicating a new mechanism for trust dynamics. Trust calibration strategies should address potential divergence.ApplicationsDesigners should consider the divergence of trust within and between teams, especially for long-duration missions. Methods to calibrate trust in this situation may include structured debriefs or shared automation feedback displays.