Estimating trust from agents' interactions via commitments

How an agent trusts another naturally depends on the outcomes of their interactions. Previous approaches have treated the outcomes in a domain-specific way. We propose an approach relating trust to the domain-independent notion of commitments. We conduct an empirical study to evaluate our approach, in which subjects read emails extracted from the Enron dataset (augmented with some synthetic emails for completeness), and estimate trust between each pair of communicating participants. We propose a probabilistic model for trust based on commitment outcomes and show how to train its parameters for each subject based on the subject's trust assessments. The results are promising, though imperfect. Our main contribution is to launch a research program into computing trust based on a semantically well-founded account of agent interactions.