Güven: estimating trust from communications

• Published in: Journal of trust management Vol. 3; no. 1; p. 1
• Main Authors: Kalia, Anup K., Zhang, Zhe, Singh, Munindar P.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 05.01.2016; Springer Nature B.V
• Subjects: Analysis; Coding and Information Theory; Commitments; Communication; Computer Science; Cooperation; Data Structures and Information Theory; Semantics; Software Engineering/Programming and Operating Systems; Studies; System theory; Systems and Data Security; Trust; Commitments; Trust; Multiagent systems
• ISSN: 2196-064X; 2196-064X
• DOI: 10.1186/s40493-015-0022-4

The extent to which an agent trusts another naturally depends on the outcomes of their interactions. Previous computational approaches have treated the outcomes in a domain-specific way. Specifically, these approaches focus on the mathematical aspect and assume that a positive or negative experience can be identified without showing how to ground the experiences in real-world interactions, such as emails and chats. We propose Güven, an approach that relates trust to the domain-independent notion of commitments. We consider commitments since commitment outcomes can be associated with experiences and a large body of works exist on commitments that include commitment representation and semantics. Also, recent research shows that commitments can be extracted from interactions, such as emails and chats. Thus, we posit Güven can provide an useful basis to infer trust between agents from their interactions. To evaluate Güven, we conducted empirical studies of two decision contexts. First, subjects read emails extracted from the Enron dataset (and augmented with some synthetic emails for completeness), and estimated trust between each pair of communicating agents. Second, the subjects played the Colored Trails game, estimating trust in their opponents. Güven incorporates a probabilistic model for trust based on commitment outcomes; we show how to train its parameters for each subject based on the subject’s 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 interpersonal interactions.