  Social, technological and economic time series are divided by events which
are usually assumed to be random albeit with some hierarchical structure. It is
well known that the interevent statistics observed in these contexts differs
from the Poissonian profile by being long-tailed distributed with resting and
active periods interwoven. Understanding mechanisms generating consistent
statistics have therefore become a central issue. The approach we present is
taken from the Continuous Time Random Walk formalism and represents an
analytical alternative to models of non-trivial priority that have been
recently proposed. Our analysis also goes one step further by looking at the
multifractal structure of the interevent times of human decisions. We here
analyze the inter-transaction time intervals of several financial markets. We
observe that empirical data describes a subtle multifractal behavior. Our model
explains this structure by taking the pausing-time density in the form of a
superstatistics where the integral kernel quantifies the heterogeneous nature
of the executed tasks. An stretched exponential kernel provides a multifractal
profile valid for a certain limited range. A suggested heuristic analytical
profile is capable of covering a broader region.
