  Leading agent-based trust models address two important needs. First, they
show how an agent may estimate the trustworthiness of another agent based on
prior interactions. Second, they show how agents may share their knowledge in
order to cooperatively assess the trustworthiness of others. However, in
real-life settings, information relevant to trust is usually obtained
piecemeal, not all at once. Unfortunately, the problem of maintaining trust has
drawn little attention. Existing approaches handle trust updates in a
heuristic, not a principled, manner. This paper builds on a formal model that
considers probability and certainty as two dimensions of trust. It proposes a
mechanism using which an agent can update the amount of trust it places in
other agents on an ongoing basis. This paper shows via simulation that the
proposed approach (a) provides accurate estimates of the trustworthiness of
agents that change behavior frequently; and (b) captures the dynamic behavior
of the agents. This paper includes an evaluation based on a real dataset drawn
from Amazon Marketplace, a leading e-commerce site.
