Abstract
The storage, processing and transmission of information is
based in an essential way on probabilistic principles.
The systems that accomplish this tasks (from error correcting
codes to communication networks) are increasingly structured
as random networks of simple interacting components.
The design and optimization of these networks requires
understanding their collective behavior. In mathematical
terms, this amounts to understanding the behavior of some
stochastic processes on large graphs.\\
I will argue that statistical mechanics concepts can play
a crucial role in this process. In particular, I will focus
on new versions of classical ideas from the theory of
Gibbs measures and phase transitions.
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