Robots are increasingly joining human teams, where collaboration and competition often coexist. However, their impact on human risk-taking remains unclear. We investigated whether collaborating with or competing against a humanoid robot influences risk-taking behavior and performance using the Balloon Analogue Risk Task (BART). In this task, participants pump a virtual balloon to earn points, but risk losing all their points if the balloon bursts. They interacted with a NAO robot in a pre-registered, mixed-design study (N=43), with interaction type (collaboration vs. competition) as a between-subjects factor and team context (solo vs. social) as a within-subjects factor. We found that social interaction increased risk behavior compared to playing alone. Competition fostered strategic risk-taking, with more pumps, fewer bursts and higher scores resulting from optimal strategy adoption. In contrast, collaboration fostered exploratory risk-taking with increased risk but without performance gains. Additional exploratory analysis revealed that men took more risks in collaboration. Our findings demonstrate that the type of social interaction, rather than the mere presence of a robot, shapes how humans take risks with robots.

Databases are highly optimized complex systems with a multitude of configuration options. Especially in cloud scenarios with thousands of database deployments, determining optimized database configurations in an automated fashion is of increasing importance for database providers. At the same time, due to increased system complexity, it becomes more challenging to identify well-performing configurations. Therefore, research interest in autonomous or self-driving database systems has increased enormously in recent years. Such systems promise both performance improvements and cost reductions. In the literature, various fully or partially autonomous optimization mechanisms exist that optimize single aspects, e.g., index selection. However, database administrators and developers often distrust autonomous approaches, and there is a lack of practical experimentation opportunities that could create a better understanding. Moreover, the interplay of different autonomous mechanisms under complex workloads remains an open question. The presented cockpit enables an interactive assessment of the impact of autonomous components for database systems by comparing (autonomous) systems with different configurations side by side. Thereby, the cockpit enables users to build trust in autonomous solutions by experimenting with such technologies and observing their effects in practice.