  Recently increased accessibility of large-scale digital records enables one
to monitor human activities such as the interevent time distributions between
two consecutive visits to a web portal by a single user, two consecutive emails
sent out by a user, two consecutive library loans made by a single individual,
etc. Interestingly, those distributions exhibit a universal behavior,
$D(\tau)\sim \tau^{-\delta}$, where $\tau$ is the interevent time, and $\delta
\simeq 1$ or 3/2. The universal behaviors have been modeled via the
waiting-time distribution of a task in the queue operating based on priority;
the waiting time follows a power law distribution $P_{\rm w}(\tau)\sim
\tau^{-\alpha}$ with either $\alpha=1$ or 3/2 depending on the detail of
queuing dynamics. In these models, the number of incoming tasks in a unit time
interval has been assumed to follow a Poisson-type distribution. For an email
system, however, the number of emails delivered to a mail box in a unit time we
measured follows a powerlaw distribution with general exponent $\gamma$. For
this case, we obtain analytically the exponent $\alpha$, which is not
necessarily 1 or 3/2 and takes nonuniversal values depending on $\gamma$. We
develop the generating function formalism to obtain the exponent $\alpha$,
which is distinct from the continuous time approximation used in the previous
studies.

Physics and Society;Statistical Mechanics