Old Strategies, New Environments: Reinforcement Learning on Social Media.

The rise of social media has profoundly altered the social world, introducing new behaviors that can satisfy our social needs. However, it is not yet known 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 (RL) can help us to precisely frame this problem and diagnose where behavior-environment mismatches emerge.

Discovering the unknown unknowns of research cartography with high-throughput natural description.

To succeed, we posit that research cartography will require high-throughput natural description to identify unknown unknowns in a particular design space. High-throughput natural description, the systematic collection and annotation of representative corpora of real-world stimuli, faces logistical challenges, but these can be overcome by solutions that are deployed in the later stages of integrative experiment design.