  Various hypotheses exist about the paths used for communication between the
nodes of complex networks. Most studies simply suppose that communication goes
via shortest paths, while others have more explicit assumptions about how
routing (alternatively navigation or search) works or should work in real
networks. However, these assumptions are rarely checked against real data. Here
we directly analyze the structure of operational paths using real measurements.
For this purpose we use existing and newly created datasets having both the
topology of the network and a sufficient number of empirically-determined paths
over it. Such datasets are processed for air transportation networks, the human
brain, the Internet and the fit-fat-cat word ladder game. Our results suggest
that from the great number of possible paths, nature seems to pick according to
some simple rules, which we will refer to as routing policies. First we
confirm, that the preference of short paths is an inevitable policy element,
however the observed stretch of the paths suggests that there are other
policies at work simultaneously. We identify two additional policies common in
our networks: the "conform hierarchy", meaning that the paths should obey the
structural hierarchy of the network, and the "prefer downstream" policy which
promotes avoiding the network core if possible. Building upon these simple
policies, we propose a synthetic routing policy which can recover the basic
statistical properties of the operational paths in networks. Our results can be
helpful in estimating the reaction of complex systems for stress coming from
the outside more accurately than the shortest path assumption permits.
