Abstract
Computational Trust and Reputation (CTR) systems are platforms capable of collecting trust information about candidate partners and of computing confidence scores for each one of these partners. These systems start to be viewed as vital elements in environments of electronic institutions, as they support fundamental decision making processes, such as the selection of business partners and the automatic and adaptive creation of contractual terms and associated enforcement methodologies. In this article, we propose a model for the aggregation of trust evidences that computes confidence scores taking into account dynamic properties of trust. We compare our model with a traditional statistical model that uses weighted means to compute trust, and show experimental results that show that in certain scenarios the consideration of the trust dynamics allows for a better estimation of confidence scores.
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Urbano, J., Rocha, A.P., Oliveira, E. (2009). Computing Confidence Values: Does Trust Dynamics Matter?. In: Lopes, L.S., Lau, N., Mariano, P., Rocha, L.M. (eds) Progress in Artificial Intelligence. EPIA 2009. Lecture Notes in Computer Science(), vol 5816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04686-5_43
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DOI: https://doi.org/10.1007/978-3-642-04686-5_43
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