  Motivated by a host of empirical evidences revealing the bursty character of
human dynamics, we develop a model of human activity based on successive
switching between an hesitation state and a decision-realization state, with
residency times in the hesitation state distributed according to a heavy-tailed
Pareto distribution. This model is particularly reminiscent of an individual
strolling through a randomly distributed human crowd. Using a stochastic model
based on the concept of anomalous and non-Markovian L\'evy walk, we show
exactly that successive decision-making processes drastically slow down the
progression of an individual faced with randomly distributed obstacles.
Specifically, we prove exactly that the average displacement exhibits a
sublinear scaling with time that finds its origins in: (i) the intrinsically
non-Markovian character of human activity, and (ii) the power law distribution
of hesitation times.
