Ranking of Direct Trust, Confidence, and Reputation in an Abstract System with Unreliable Components

Trust is an important aspect in human societies. It enables cooperation and provides means to estimate potential cooperation partners. Several works have addressed how the concept of trust can be transferred to computer systems. In this paper, we present an approach to calculate trust, including direct trust, confidence, and reputation, in a network consisting of agents with changing behavior. Our metrics are highly configurable for an adaption to a wide variety of systems and situations, especially Organic Computing Systems can benefit from trust by integrating it in their algorithms implementing self-organizational behavior. We evaluate the effect of direct trust and confidence together with reputation (DTCR) in comparison with using only direct trust (DT) or direct trust with confidence (DTC). Because these metrics can be configured with many parameters leading to an immense number of possible configurations we apply a heuristic optimization algorithm to find very good setups showing the highest benefits. For this evaluation, an abstract scenario is developed and applied, it consists of unreliable components from different classes of defined mean behavior. This general scenario could model many possible industrial settings out of which a few are introduced, too. Our evaluations show that reputation and direct trust are best used together with a fluent transition between them defined by the confidence. In all cases, reputation works as a corrective when direct trust information is not optimal and potentially misleading. This leads to very good results with very limited variance, particularly we show that a small number of interactions are sufficient to obtain the best results.