Dynamically learning sources of trust information: experience vs. reputation

ABSTRACT

Trust is essential when an agent must rely on others to provide resources for accomplishing its goals. When deciding whether to trust, an agent may rely on, among other types of trust information, its past experience with the trustee or on reputations provided by third-party agents. However, each type of trust information has strengths and weaknesses: trust models based on past experience are more certain, yet require numerous transactions to build, while reputations provide a quick source of trust information, but may be inaccurate due to unreliable reputation providers. This research examines how the accuracy of experience- and reputation-based trust models is influenced by parameters such as: frequency of transactions with the trustee, trustworthiness of the trustee, and accuracy of provided reputations. More importantly, this research presents a technique for dynamically learning the best source of trust information given these parameters. The demonstrated learning technique achieves payoffs equal to those achieved by the best single trust information source (experience or reputation) in nearly every scenario examined.

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INDEX TERMS

General Terms:
Algorithms, Experimentation

Keywords:
learning, multiagent systems, reputation, trust