zTrust: adaptive decentralized trust model for QoS selection in electronic marketplaces
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Date
2013
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Publisher
University of New Brunswick
Abstract
In the absence of legal enforcement procedures for the participants of an
open e-marketplace, trust and reputation systems are central to resisting
threats from malicious agents. Such systems also provide mechanisms for
identifying participants who disseminate unfair ratings. This thesis research
is in the area of modeling trust in multi-agent systems where self-interested
agents intelligently interact to maximize their benefits. The thesis presents
an adaptive intelligent trust model, called zTrust, with multidisciplinary approaches
well-suited for cooperative and competitive electronic commerce in
such a way that individuals could make optimal decisions in selecting business
transaction partners.
zTrust has two essential elements: 1) a two-layered filtering algorithm, the
Prob-Cog model, with an adaptive threshold evaluation procedure, which
combines cognitive and probabilistic views of trust to classify participants
through modelling their subjectivity, behavioural characteristics and environmental
conditions in order to detect unfair advisers, and 2) a two-layered
Trust-Oriented Service Selection (TOSS) framework to assist consumers in discovering providers who maximize their utility by achieving sufficient trust
and fulfilling consumers' preferences on product quality.
We also propose a trust-oriented mechanism built on a game-theoretic basis
well-suited for competitive e-marketplaces where providers might have
limited inventory. The characteristics of such an environment make consumers
concerned with the possibility of losing the opportunity to do business
with good providers whilst providing truthful reputation information about
providers. The proposed mechanism provides consumers with a means to
strategically determine their reporting behaviour by establishing a balance
between the possibility of losing business opportunities because of truthful
reporting and the possibility of not receiving truthful provider information
from advisers if the consumers report untruthfully.
We provide a series of experimental results in a simulated dynamic environment
where agents may be arriving and departing. We illustrate the efficacy
and the robustness of our approaches in important conditions of the cooperative
and competitive marketplaces, as well as in comparison with other
approaches. We observe that zTrust enables consumers to access more accurate
information, even in a hostile environment, and enjoy high profits as
a result of interacting with high-quality providers. Furthermore, our mechanism
promotes honesty amongst providers as dishonest providers would be
isolated and would lose the opportunity to be selected as transaction partners.