An adaptive evaluation mechanism for online traders

Economic agents in electronic markets generally consider reputation to be a significant factor in selecting trading partners. Most traditional online businesses publish reputation profile for traders that reflect average of the ratings received in previous transactions. Because of the importance of these ratings, there is an incentive for traders to partake in strategic behavior (for example shilling) to artificially inflate their rating. It is therefore important for an online business to be able to provide a robust estimate of a trader's reputation that is not easily affected by strategic behavior or noisy ratings. This paper proposes such an adaptive ratings-based reputation model. The model is based on a trader's transaction history, witness testimony, and other weighting factors. Learning is integrated to make the ratings model adaptive and robust in a dynamic environment. To validate the proposed model and to demonstrate the significance of its constructs, a multi-agent system is built to simulate the interactions among buyers and sellers in an electronic marketplace. The performance of the proposed model is compared to that of the reputation model used in most online marketplaces like Amazon, and to Huynh's model proposed in the literature.