Agency Is Frame-Dependent
This addresses a foundational puzzle in multiple fields like AI and philosophy, but it is incremental as it builds on existing theories without introducing new empirical results.
The paper tackles the problem of determining whether a system exhibits agency by arguing that agency is frame-dependent, meaning any measurement must be relative to a reference frame, and supports this with philosophical arguments about essential properties of agency.
Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science, and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.