Summary

Kostas Triantis has received a four-year $2 million National Science Foundation grant to explore how cognitive biases influence trust in automation and decisions to delegate tasks to automated technologies. 

Read more about our ongoing research on automation in safety-critical systems here.

Not all organizational problems are simple puzzles with fixed structures and predictable processes. Today's challenges arise from dynamic interactions among organizational structures, evolving procedures, and shifting outcomes. Our research offers actionable guidance for navigating complex challenges and shaping adaptive policies in an era of rapid digital transformation.

We’re partnering with Human Factors specialists from Clemson University, who have conducted rigorous eye‑tracking and mental workload experiments. Together we’re developing a holistic framework that maps out work‑system elements, their interrelated processes (including how knowledge is acquired and shared), and the emergence of distributed situational awareness. By connecting situational awareness to action execution, our model quantifies its impact on efficiency, safety, and operator workload—guiding design improvements that optimize real‑world outcomes.

In partnership with Georgetown University and Brigham Young University, we investigate how both organizations and individuals learn from automation use in high‑stakes environments. Our research examines macro‑level organizational learning processes alongside micro‑level user experience and behavior, using Infrabel’s traffic control centers, the nerve center of Belgium’s railway infrastructure, as a real‑world testbed. We aim to drive safer, more efficient, and more resilient rail operations by uncovering how automation shapes knowledge, decision-making, and practices.

We leverage system dynamics modeling to model the underlying dynamics in Rasmussen’s framework of safety, economic and workload boundaries. We explore the key trade‑offs between the different boundaries using causal loop diagrams. These trade-offs include, but are not limited to: (i) intuition versus automation, (ii) team familiarity versus flexibility, (iii) generalization versus specialization of roles, and (iv) competing goal hierarchies, which shape both individual behavior and organizational outcomes.