  We introduce a general framework for exploring the problem of selecting a
committee of representatives with the aim of studying a networked voting rule
based on a decentralized large-scale platform, which can assure a strong
accountability of the elected. The results of our simulations suggest that this
algorithm-based approach is able to obtain a high representativeness for
relatively small committees, performing even better than a classical voting
rule based on a closed list of candidates. We show that a general relation
between committee size and representatives exists in the form of an inverse
square root law and that the normalized committee size approximately scales
with the inverse of the community size, allowing the scalability to very large
populations. These findings are not strongly influenced by the different
networks used to describe the individuals interactions, except for the presence
of few individuals with very high connectivity which can have a marginally
negative effect in the committee selection process.
