In the context of Smart Ecosystems, systems engage in dynamic cooperation with other systems to achieve their goals. Expedient operation is only possible when all systems cooperate as expected. This requires a level of trust between the components of the ecosystem. New systems that join the ecosystem therefore first need to build up a level of trust. Humans derive trust from behavioral reputation in key situations. In Smart Ecosystems (SES), the reputation of a system or system component can also be based on observation of its behavior. In this paper, we introduce a method and a test platform that support virtual evaluation of decisions at runtime, thereby supporting trust building within SES. The key idea behind the platform is that it employs and evaluates Digital Twins, which are executable models of system components, to learn about component behavior in observed situations. The trust in the Digital Twin then builds up over time based on the behavioral compliance of the real system component with its Digital Twin. In this paper, we use the context of automotive ecosystems and examine the concepts for building up reputation on control algorithms of smart agents dynamically downloaded at runtime to individual autonomous vehicles within the ecosystem.
