Old strategies, new environments: Reinforcement Learning on social media

The rise of social media has profoundly altered the social world - introducing new behaviours which can satisfy our social needs. However, it is yet unknown whether human social strategies, which are well-adapted to the offline world we developed in, operate as effectively within this new social environment. Here, we describe how the computational framework of Reinforcement Learning can help us to precisely frame this problem and diagnose where behaviour-environment mismatches emerge. The Reinforcement Learning framework describes a process by which an agent can learn to maximise their long-term reward. Reinforcement Learning, which has proven successful in characterising human social behaviour, consists of three stages: updating expected reward, valuating expected reward by integrating subjective costs such as effort, and selecting an action. Specific social media affordances, such as the quantifiability of social feedback, might interact with the Reinforcement Learning process at each of these stages. In some cases, affordances can exploit Reinforcement Learning biases which are beneficial offline, by violating the environmental conditions under which such biases are optimal - such as when algorithmic personalisation of content interacts with confirmation bias. Characterising the impact of specific aspects of social media through this lens can improve our understanding of how digital environments shape human behaviour. Ultimately, this formal framework could help address pressing open questions about social media use, including its changing role across human development, and its impact on outcomes such as mental health.