Abstract
As Web-based online communities are rapidly growing, the agents in social groups need to know their measurable belief of trust for safe and successful interactions. In this paper, we propose a computational model of trust resulting from available feedbacks in online communities. The notion of trust can be defined as an aggregation of consensus given a set of past interactions. The average trust of an agent further represents the center of gravity of the distribution of its trustworthiness and untrustworthiness. And then, we precisely describe the relationship between reputation, trust, and average trust through a concrete example of their computations. We apply our trust model to online Internet settings in order to show how trust mechanisms are involved in a rational decision-making of the agents.
This work has been supported by the Catholic University of Korea research fund, 2006, department specialization fund, 2007, and by the Agency for Defense Development under Grant UD060072FD “A Study on the Multi-Spectral Threat Data Integration of ASE,” 2006.
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Noh, S. (2007). Calculating Trust Using Aggregation Rules in Social Networks. In: Xiao, B., Yang, L.T., Ma, J., Muller-Schloer, C., Hua, Y. (eds) Autonomic and Trusted Computing. ATC 2007. Lecture Notes in Computer Science, vol 4610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73547-2_38
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DOI: https://doi.org/10.1007/978-3-540-73547-2_38
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