  We study finite-size effects on the convergence time in a continuous-opinion
dynamics model. In the model, each individual's opinion is represented by a
real number on a finite interval, e.g., $[0,1]$, and a uniformly randomly
chosen individual updates its opinion by partially mimicking the opinion of a
uniformly randomly chosen neighbor. We numerically find that the characteristic
time to the convergence increases as the system size increases according to a
particular functional form in the case of lattice networks. In contrast, unless
the individuals perfectly copy the opinion of their neighbors in each opinion
updating, the convergence time is approximately independent of the system size
in the case of regular random graphs, uncorrelated scale-free networks, and
complete graphs. We also provide a mean-field analysis of the model to
understand the case of the complete graph.
