  With the increasing abundance of 'digital footprints' left by human
interactions in online environments, e.g., social media and app use, the
ability to model complex human behavior has become increasingly possible. Many
approaches have been proposed, however, most previous model frameworks are
fairly restrictive. We introduce a new social modeling approach that enables
the creation of models directly from data with minimal a priori restrictions on
the model class. In particular, we infer the minimally complex, maximally
predictive representation of an individual's behavior when viewed in isolation
and as driven by a social input. We then apply this framework to a
heterogeneous catalog of human behavior collected from fifteen thousand users
on the microblogging platform Twitter. The models allow us to describe how a
user processes their past behavior and their social inputs. Despite the
diversity of observed user behavior, most models inferred fall into a small
subclass of all possible finite-state processes. Thus, our work demonstrates
that user behavior, while quite complex, belies simple underlying computational
structures.
