Human-Machine Teaming, Proactive and Anticipatory Safety, Visual Analytics, Systems Complexity Science: What do they mean?
Resilient Human-Machine Teaming
Rather than the traditional narrative that automation can be substituted for humans, we believe the integration of technology into work changes workflow and human operators' cognitive demands. Incorporating systems principles of teamwork - shared common ground, basic agreements to coordinate to achieve a purpose, and mutual observability and directability - into design improves the performance of entire human-machine systems and creates graceful extensibility beyond the edges of normal operating conditions.
Proactive and Anticipatory Safety
Outcomes are a function of system elements and their interactions: policies, procedures, training, tools, management, organizational directives, and more. "Human error", commonly blamed for poor outcomes, is a symptom of system design that cannot be adequately addressed through punishment and attitude-improvement campaigns. Instead, we assert that people are the integral component of safe workspaces, and that increasing complexity creates the conditions for failure. Complex system safety can be improved by supporting the operators' abilities to make sense of events and by expanding the system's capacity to adapt. To be truly proactive, we leverage instances of operations going well in the face of difficult circumstances as well as those that fail to learn and improve before the next major accident.
Data become informative or meaningful based on their relationships to larger frames of reference and the interests and expectations of the observer. We work with subject matter experts to understand these valuable relationships, then design innovative displays that aid viewers' sensemaking. Visualizations can enable operators to cope with complicating factors such as data overload and missing or misleading information to ultimately make decisions while under pressure.
Systems Complexity Science
We ground our work in theories of complex systems, characterized by high tempo, dynamic conditions, and distributed responsibility and authority. We further our field's understanding of adaptive system behaviors, as well as self-regulation and self-organization, the foundations of which are resilience and predictors of system failure. These patterns are often consistent across work domains such that they enable insights on coping with complexity and creating resilience.