  Appropriate ranking algorithms and incentive mechanisms are essential to the
creation of high-quality information by users of a social network. However,
evaluating such mechanisms in a quantifiable way is a difficult problem.
Studies of live social networks of limited utility, due to the subjective
nature of ranking and the lack of experimental control. Simulation provides a
valuable alternative: insofar as the simulation resembles the live social
network, fielding a new algorithm within a simulated network can predict the
effect it will have on the live network. In this paper, we propose a simulation
model based on the actor-conceptinstance model of semantic social networks,
then we evaluate the model against a number of common ranking algorithms.We
observe their effects on information creation in such a network, and we extend
our results to the evaluation of generic ranking algorithms and incentive
mechanisms.
