Old Theories, New Teams: Advancing Team Adaptation Theory for Human-Autonomy-Teams
TL;DR
This work identifies four key conditions—predictability, observability, plannability, and directability—that govern how HATs respond to shifting interdependencies and proposes a holistic framework that integrates the dynamic interplay between team members, tasks, and the environment.
Abstract
Human–Autonomy Teams (HATs), composed of humans and agents such as artificial intelligence, face distinctive challenges in dynamic environments. As a result, they often struggle to adapt effectively, limiting their potential. Traditional team theories do not fully capture the unique characteristics of HATs, and research remains fragmented across disciplines, offering limited theoretical and practical guidance. Consequently, we propose a holistic framework that integrates the dynamic interplay between team members, tasks, and the environment. Drawing on multidisciplinary insights, we identify four key conditions—predictability, observability, plannability, and directability—that govern how HATs respond to shifting interdependencies. For each condition, we advance testable propositions, illustrate their relevance through established HAT testbeds, and outline technical, organizational, and interactional strategies to foster their emergence. Finally, we discuss how the framework provides a conceptual foundation to guide future research, training, and the design of adaptive HATs, by reflecting on its broader theoretical and practical implications.