Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We analyze the convergence of opinions or beliefs in a general social network with non-Bayesian agents. We provide a new sufficient condition under which opinions converge to consensus and the condition is significantly more permissive than that of Lorenz (2005). This condition, which depends on properties of the network, requires agents to incorporate others’ opinions into their own posterior sufficiently often.