  Decentralized trust management is used as a referral benchmark for assisting
decision making by human or intelligence machines in open collaborative
systems. During any given period of time, each participant may only interact
with a few of other participants. Simply relying on direct trust may frequently
resort to random team formation. Thus, trust aggregation becomes critical. It
can leverage decentralized trust management to learn about indirect trust of
every participant based on past transaction experiences. This paper presents
alternative designs of decentralized trust management and their efficiency and
robustness from three perspectives. First, we study the risk factors and
adverse effects of six common threat models. Second, we review the
representative trust aggregation models and trust metrics. Third, we present an
in-depth analysis and comparison of these reference trust aggregation methods
with respect to effectiveness and robustness. We show our comparative study
results through formal analysis and experimental evaluation. This comprehensive
study advances the understanding of adverse effects of present and future
threats and the robustness of different trust metrics. It may also serve as a
guideline for research and development of next generation trust aggregation
algorithms and services in the anticipation of risk factors and mischievous
threats.
