Agency Is Frame-Dependent

TL;DR

This paper argues that agency, the capacity of a system to influence outcomes, is inherently frame-dependent, affecting how we measure and understand agency across various scientific disciplines.

Contribution

It introduces the idea that agency measurement must be relative to a reference frame, supported by philosophical arguments and implications for reinforcement learning.

Findings

Agency properties are frame-dependent.

Measurement of agency requires a reference frame.

Implications for reinforcement learning models.

Abstract

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.