  Recent research shows that humans are heavily influenced by online social
interactions: We are more likely to perform actions which, in the past, have
led to positive social feedback. We introduce a quantitative model of behavior
changes in response to such feedback, drawing on inverse reinforcement learning
and studies of human game playing. The model allows us to make predictions,
particularly in the context of social media, about which community a user will
select, and to quantify how future selections change based on the feedback a
user receives. We show that our model predicts real-world changes in behavior
on a dataset gathered from reddit. We also explore how this relatively simple
model of individual behavior can lead to complex collective dynamics when there
is a population of users, each individual learning in response to feedback and
in turn providing feedback to others.
