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, pro-vide opportunities for human users represented as agents to report their ratings or opin-ions 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, buy-ers 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, th