A Subjective Metric of Authentication
ESORICS '98 Proceedings of the 5th European Symposium on Research in Computer Security
Formal Analysis of Models for the Dynamics of Trust Based on Experiences
MAAMAW '99 Proceedings of the 9th European Workshop on Modelling Autonomous Agents in a Multi-Agent World: MultiAgent System Engineering
Emergent properties of referral systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Trust network analysis with subjective logic
ACSC '06 Proceedings of the 29th Australasian Computer Science Conference - Volume 48
A survey of trust and reputation systems for online service provision
Decision Support Systems
An adaptive probabilistic trust model and its evaluation
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Formal trust model for multiagent systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Evidence-based trust in distributed agent systems
Evidence-based trust in distributed agent systems
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We describe and model a new aspect in the design of distributed information systems. We build upon a previously described problem on the microlevel, which asks how quickly agents should discount (forget) their experience: If they cherish their memories, they can build their reports on larger data sets; if they discount quickly, they can respond well to change in their environment. Here, we argue that on the macro-level, where agents disseminate information, the coordination of these micro-level strategies of discounting can have significant consequences on the system performance if the environment is uncertain. In our proposed model, a referral network disseminates information about a disruptive environment (a service provider) to a risk-averse client agent, who uses this information to maximise his profit and then gives feedback into the referral system. We model two simple strategies to dynamically find better discounting factors, through central and decentral control. We show that with dynamic discounting rates, the system can become more reactive. We discuss interdependence of the system components in the light of differing discounting scenarios. In this work, we build on a certainty-based trust representation and operators for it in referral systems, developed by Josang [7] and Hang, Wang and Singh [13,2].