Computational Trust - Discussion On Trust/Reputation Models

Discussion On Trust/Reputation Models

The most relevant sources of information considered by the trust and reputation models presented before, are direct experiences and witness information. In the actual e-markets, the sociological information is almost non-existent and, in order to increase the efficiency of actual Trust and reputation models, it should be considered. However, there is no sense to increase the complexity of models introducing trust evidences if, later, they have to be used in an environment where it is not possible to exploit their capabilities. The aggregation of more Trust and reputation evidences is useful in a computational model but it can increase its complexity and so it is difficult to find a general solution. Several models are extremely dependent on the characteristics of the environment and a possible solution could be the use of adaptive mechanisms that can modify how to combine different sources of information in a given environment. A lot of trust and reputation definitions have been presented and there are several works that help to give a precise and distinct meaning of both concepts.

There is a relation between both the concepts that should be considered in depth: reputation is a concept that helps to build trust on others. Nowadays, game theory is the predominant paradigm considered to design computational trust and reputation models. In all likelihood, this theory is taken into account because a significant number of economists and computer scientists, with a strong background in game theory and artificial intelligence techniques, are working in multi-agent and e-commerce contexts. Game theoretical models produce good results but, when the complexity of the agents, in terms of social relations and interaction increases, become too restrictive. The exploration of new possibilities should be considered and, for example, there should be an aggregation between cognitive approaches with game theoretical ones. Apart from that, more trust evidences should be considered and trust metrics more sensitive to time, represent the first step to encourage the improvement of computational trust.

An important issue in modeling trust is represented by the transferability of trust judgements by different agents. Social scientists agree to consider unqualified trust values not transferable, but a more pragmatic approach would conclude that qualified trust judgments are worth being transferred as far as decisions taken considering others’ opinion are better than the ones taken in isolation. In the authors investigated the problem of trust transferability in open distributed environments, proposing a translation mechanism able to make information exchanged from one agent to another more accurate and useful.