  Preference rankings virtually appear in all field of science (political
sciences, behavioral sciences, machine learning, decision making and so on).
The well-know social choice problem consists in trying to find a reasonable
procedure to use the aggregate preferences expressed by subjects (usually
called judges) to reach a collective decision. This problem turns out to be
equivalent to the problem of estimating the consensus (central) ranking from
data that is known to be a NP-hard Problem. Emond and Mason in 2002 proposed a
branch and bound algorithm to calculate the consensus ranking given $n$
rankings expressed on $m$ objects. Depending on the complexity of the problem,
there can be multiple solutions and then the consensus ranking may be not
unique. We propose a new algorithm to find the consensus ranking that is
equivalent to Emond and Mason's algorithm in terms of at least one of the
solutions reached, but permits a really remarkable saving in computational
time.
