  An important aspect of AI design and ethics is to create systems that reflect
aggregate preferences of the society. To this end, the techniques of social
choice theory are often utilized. We propose a new social choice function
motivated by the PageRank algorithm. The function ranks voting options based on
the Condorcet graph of pairwise comparisons. To this end, we transform the
Condorcet graph into a Markov chain whose stationary distribution provides the
scores of the options. We show how the values in the stationary distribution
can be interpreted as quantified aggregate support for the voting options, to
which the community of voters converges through an imaginary sequence of
negotiating steps. Because of that, we suggest the name "convergence voting"
for the new voting scheme, and "negotiated community support" for the resulting
stationary allocation of scores.
  Our social choice function can be viewed as a consensus voting method,
sitting somewhere between Copeland and Borda. On the one hand, it does not
necessarily choose the Condorcet winner, as strong support from a part of the
society can outweigh mediocre uniform support. On the other hand, the influence
of unpopular candidates on the outcome is smaller than in the primary technique
of consensus voting, i.e., the Borda count. We achieve that without having to
introduce an ad hoc weighting that some other methods do.
