  Current trust and reputation models continue to have significant limitations,
such as the inability to deal with agents constantly entering or exiting open
multi-agent systems (open MAS), as well as continuously changing behaviors. Our
study is based on CA, a previously proposed decentralized computational trust
model from the trustee's point of view, inspired by synaptic plasticity and the
formation of assemblies in the human brain. It is designed to meet the
requirements of highly dynamic and open MAS, and its main difference with most
conventional trust and reputation models is that the trustor does not select a
trustee to delegate a task; instead, the trustee determines whether it is
qualified to successfully execute it. We ran a series of simulations to compare
CA model to FIRE, a well-established, decentralized trust and reputation model
for open MAS under conditions of continuous trustee and trustor population
replacement, as well as continuous change of trustees' abilities to perform
tasks. The main finding is that FIRE is superior to changes in the trustee
population, whereas CA is resilient to the trustor population changes. When the
trustees switch performance profiles FIRE clearly outperforms despite the fact
that both models' performances are significantly impacted by this environmental
change. Findings lead us to conclude that learning to use the appropriate trust
model, according to the dynamic conditions in effect could maximize the
trustor's benefits.
