  We study a model of consensus decision making, in which a finite group of
Bayesian agents has to choose between one of two courses of action. Each member
of the group has a private and independent signal at his or her disposal,
giving some indication as to which action is optimal. To come to a common
decision, the participants perform repeated rounds of voting. In each round,
each agent casts a vote in favor of one of the two courses of action,
reflecting his or her current belief, and observes the votes of the rest.
  We provide an efficient algorithm for the calculation the agents have to
perform, and show that consensus is always reached and that the probability of
reaching a wrong decision decays exponentially with the number of agents.
