  The recent availability of electronic datasets containing large volumes of
communication data has made it possible to study human behavior on a larger
scale than ever before. From this, it has been discovered that across a diverse
range of data sets, the inter-event times between consecutive communication
events obey heavy tailed power law dynamics. Explaining this has proved
controversial, and two distinct hypotheses have emerged. The first holds that
these power laws are fundamental, and arise from the mechanisms such as
priority queuing that humans use to schedule tasks. The second holds that they
are a statistical artifact which only occur in aggregated data when features
such as circadian rhythms and burstiness are ignored. We use a large social
media data set to test these hypotheses, and find that although models that
incorporate circadian rhythms and burstiness do explain part of the observed
heavy tails, there is residual unexplained heavy tail behavior which suggests a
more fundamental cause. Based on this, we develop a new quantitative model of
human behavior which improves on existing approaches, and gives insight into
the mechanisms underlying human interactions.
