  Collective robotic systems are biologically inspired and advantageous due to
their apparent global intelligence and emergent behaviors. Many applications
can benefit from the incorporation of collectives, including environmental
monitoring, disaster response missions, and infrastructure support.
Transparency research has primarily focused on how the design of the models,
visualizations, and control mechanisms influence human-collective interactions.
Traditionally most evaluations have focused only on one particular system
design element, evaluating its respective transparency. This manuscript
analyzed two models and visualizations to understand how the system design
elements impacted human-collective interactions, to quantify which model and
visualization combination provided the best transparency, and provide design
guidance, based on remote supervision of collectives. The consensus
decision-making and baseline models, as well as an individual agent and
abstract visualizations, were analyzed for sequential best-of-n decision-making
tasks involving four collectives, composed of 200 entities each. Both models
and visualizations provided transparency and influenced human-collective
interactions differently. No single combination provided the best transparency.
