| Abstract | Agent-based modeling has been extensively used by scientists to study complex systems.Participatory simulations are similar to agent-based models except that humans play the
role of the virtual agents. The Bifocal modeling approach uses sensors to gather data
about the real-world phenomena being modeled and uses that information to affect the
model. In this work, we are interested in automatically extracting, analyzing and
modeling group behaviors in problem solving. Combining these three systems into one
unified platform would be useful for those purposes, since it would facilitate a synthesis
of their main affordances: understanding the role of locality, mapping human action to
emergent behaviors, and controlling embedded physical objects in noisy environments
while receiving sensory feedback. We will demonstrate a technological platform based on
the NetLogo/HubNet architecture that supports simulated agents, participatory agents and
physical agents. We place this platform within a more general framework that we call
Human, Embedded and Virtual agents in Mediation (HEV-M). We have run several
studies using an instantiation of this platform that consists of a robot-car with four users
who navigate a maze. We believe that this tool has potential for three main reasons (1) it
facilitates logging of participant’s actions, so as to identify patterns, (2) it offers
researchers in the field of computer-supported collaborative learning an easy-to-use tool
to design engaging collaborative learning activities and, (3) it foregrounds the role of
individual actions within the accomplishment of a collective goal, highlighting the
connections between simple individual actions and the resultant macroscopic behaviors
of the system.
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