Reputation systems take as input ratings from members in a community, and can produce measures of reputation, trustworthiness or reliability of entities in the same community. Binomial and multinomial Bayesian reputation systems are discrete in nature meaning that they normally take discrete ratings such as "average" or "good" as input. However, in many situations it is natural to provide input ratings to reputation systems based on continuous measures. This paper describes the principles of discrete Bayesian reputation systems, and how continuous measures can provide input ratings to such systems. The method is based on fuzzy set membership functions.
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.
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