  Queueing theory has been recently proposed as a framework to model the heavy
tailed statistics of human activity patterns. The main predictions are the
existence of a power-law distribution for the interevent time of human actions
and two decay exponents $\alpha=1$ and $\alpha=3/2$. Current models lack,
however, a key aspect of human dynamics, i.e. several tasks require, or are
determined by, interactions between individuals. Here we introduce a minimal
queueing model of human dynamics that already takes into account human-human
interactions. To achieve large scale simulations we obtain a coarse-grained
version of the model, allowing us to reach large interevent times and reliable
scaling exponents estimations. Using this we show that the interevent
distribution of interacting tasks exhibit the scaling exponents $\alpha=2$, 3/2
and a series of numerable values between 3/2 and 1. This work demonstrates
that, within the context of queueing models of human dynamics, interactions
change the exponent of the power-law distributed interevent times. Beyond the
study of human dynamics, these results are relevant to systems where the event
of interest consists of the simultaneous occurrence of two (or more) events.
