Presentation
67. Credible Emergence: Leveraging Bio-Behavioral Data in Agent-Based Models for Dynamic Trust Studies
SessionPoster Session 2
DescriptionThis poster advocates for the integration of a mind-motor-machine interaction framework within agent-based models (ABM) for trust formation. This method, applied to cognitive states, allows us to consider asymmetric processes, individual trust trajectories, non-linear updates of agent belief states, and emergent properties. As such, completing the perception-cognition-action loop warrants employing bio-behavioral data.
Through agent-based modeling one can generate hypothetical scenarios that reflect expected human behavior or experiences, reminiscent of the concept of a “digital twin.” We argue the necessity of granular data, such as neurophysiological or gaze data, for understanding trust-specific cognitive and affective processes. Such bio-behavioral data types are key for designing decision architectures that elicit credible emergent properties.
We present the mechanisms and neural correlates of trust and how they can be modeled with ABM. Such insights can inspire not only design, but also inquiry regarding driver takeover and non-driving task behavior.
Through agent-based modeling one can generate hypothetical scenarios that reflect expected human behavior or experiences, reminiscent of the concept of a “digital twin.” We argue the necessity of granular data, such as neurophysiological or gaze data, for understanding trust-specific cognitive and affective processes. Such bio-behavioral data types are key for designing decision architectures that elicit credible emergent properties.
We present the mechanisms and neural correlates of trust and how they can be modeled with ABM. Such insights can inspire not only design, but also inquiry regarding driver takeover and non-driving task behavior.
Event Type
Poster
TimeThursday, September 12th5:30pm - 6:30pm MST
LocationMcArthur Ballroom
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