  Social mediator robots facilitate human-human interactions by producing
behavior strategies that positively influence how humans interact with each
other in social settings. As robots for social mediation gain traction in the
field of human-human-robot interaction, their ability to "understand" the
humans in their environments becomes crucial. This objective requires models of
human understanding that consider multiple humans in an interaction as a
collective entity and represent the group dynamics that exist among its
members. Group dynamics are defined as the influential actions, processes, and
changes that occur within and between group interactants. Since an individual's
behavior may be deeply influenced by their interactions with other group
members, the social dynamics existing within a group can influence the
behaviors, attitudes, and opinions of each individual and the group as a whole.
Therefore, models of group dynamics are critical for a social mediator robot to
be effective in its role. In this paper, we survey existing models of group
dynamics and categorize them into models of social dominance, affect, social
cohesion, conflict resolution, and engagement. We highlight the multimodal
features these models utilize, and emphasize the importance of capturing the
interpersonal aspects of a social interaction. Finally, we make a case for
models of relational affect as an approach that may be able to capture a
representation of human-human interactions that can be useful for social
mediation.
