Towards the design of robust incentive mechanisms to address subjectivity and dishonesty problems in agent reporting

Agent reporting systems, such as reputation systems and crowdsourcing platforms, provide opportunities for human users represented as agents to report their ratings or opinions via electronic channels. However, their reports could vary and are difficult to be aggregated (subjectivity problem) and they may provide untruthful reports (dishonesty problem). The two challenging problems have attracted considerable interests from the academia and industry. The first objective of this thesis is to address the subjectivity and dishonesty problems through designing three incentive mechanisms where the agents are assumed to be rational. Furthermore, considering the existence of bounded rational agents in real systems, the second objective is to evaluate the robustness of incentive mechanisms against bounded rational agents. Specifically, to address the subjectivity problem, a coalition formation game based reputation system (CONGRESS) is proposed for e-marketplaces. In CONGRESS, buyers with the same subjectivity are incentivized to form a separate club to build seller reputation based on the ratings collected from the buyers in the club. Consequently, the proposed model avoids suffering from the bad effect of the subjectivity problem. It has been theoretically proven that buyers with the same subjectivity have the incentive to form a same club if a certain condition is satisfied. Furthermore, a set of experiments have been conducted to validate the proposed reputation system. Secondly, to address the dishonesty problem, an incentive mechanism iMEMLI is proposed for e-marketplaces with limited inventory where the supply of sellers is less