Year: 2026
Type: article
Abstract: AI provides a rich source of examples for theories of agency because it allows relevant features to be relatively freely combined. This paper considers agency in sequence models: models that have been trained to predict the next item in a sequence. Training models on sequences of observations and actions can yield systems that use reinforcement learning in context, as well as ones that merely imitate agents. Investigating systems from this group suggests a requirement that agents must learn about the environment in part from their own interaction with it. It also suggests a distinction between two approaches to understanding agency.
Source: Philosophy of Science
Author: Patrick Butlin
Institution: University of Oxford
Language: English
Subfield: History and Philosophy of Science
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,550
OpenAlex ID: W7169564313
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