  We discuss a set of computational techniques, called Inductive Game Theory,
for extracting strategic decision-making rules from time series data and
constructing probabilistic social circuits. We construct these circuits by
connecting component individuals and groups with strategies in a game and
propose an inductive approach to reconstructing the edges. We demonstrate this
approach with conflict behavior in a society of pigtailed macaques by
identifying significant patterns in decision-making by individuals. With the
constructed circuit, we then capture macroscopic features of the system that
were not specified in the construction of the initial circuit, providing a
mapping between individual level behaviors to collective behaviors over the
scale of the group. We extend on previous work in Inductive Game Theory by more
efficiently searching the space of possible strategies by grouping individuals
into socially relevant sets to produce a more efficient, parsimonious
specification of the underlying interactions between components. We discuss how
we reduce the dimensionality of these circuits using coarse-graining or
compression to build cognitive effective theories for collective behavior.

Social and Information Networks;Physics and Society