Modeling the rational behavior of individuals on an e-commerce system

With the increasing popularity of e-commerce systems, commercial transactions are becoming more and more frequent. Such transactions are not direct but mediated, putting the buyer in a position of weakness with respect to the seller, especially in the case of a failure of a transaction. The literature showed that the reputation can play an important role to reduce the risks of the buyer in the current e-commerce environment. An online reputation management system (RMS) maintains the reputation, made of beliefs and/or opinions, that are generally held about someone or something, and it can guarantee the reliability of the transactions that take place in an e-commerce system. Despite of the fact that the basic element of a RMS – the interaction between the seller and the buyer – is a classical field of application of the Game Theory (GT) methodologies, the use of a GT approach in this context seems quite limited and this is probably due to its solution complexity. A way to deal with such a complexity is by exploiting the capability of the agent based simulation (ABS) approach. In this paper, we propose a hybrid GT and ABS model for the analysis of an e-commerce system in which a centralized reputation system is maintained by a trusted third party. We report an extensive quantitative analysis in order to validate the proposed model, and to evaluate the impact of a set of buyers’ and sellers’ policies on the behavior of the e-commerce system