Learning to Trust on the Move

Computational trust has been developed as a novel means of coping with uncertainty within collaborative communities of interacting peers. The idea now offers enourmous potential for use in pervasive mobile environments; however, to date there is little agreement about what computational trust itself means, and what the limitations that emerge from its use are. In this work, we project the idea of computational trust into machine learning terms, showing that trust is a metaphor that helps system designers reason about and exploit the intended deployment scenario to achieve their goals. Viewing a trust model as a strategy to confront a learning problem thus allows us to explore the effect that constraints, such as mobility and user participation, will have on the quantity of information available to learn from; in this work, we demonstrate this idea with a set of experiments on the Reality Mining Dataset. The results highlight that the most successful trust models will be based on strong contextual information about the environment they are to be deployed in.