  "If we know more, we can achieve more." This adage also applies to
communication networks, where more information about the network state
translates into higher sumrates. In this paper, we formalize this increase of
sum-rate with increased knowledge of the network state. The knowledge of
network state is measured in terms of the number of hops, h, of information
available to each transmitter and is labeled as h-local view. To understand how
much capacity is lost due to limited information, we propose to use the metric
of normalized sum-capacity, which is the h-local view sum-capacity divided by
global-view sum capacity. For the cases of one and two-local view, we
characterize the normalized sum-capacity for many classes of deterministic and
Gaussian interference networks. In many cases, a scheduling scheme called
maximal independent graph scheduling is shown to achieve normalized
sum-capacity. We also show that its generalization for 1-local view, labeled
coded set scheduling, achieves normalized sum-capacity in some cases where its
uncoded counterpart fails to do so.
