General Social Agents

Understand

Useful social science theories predict behavior across settings.

• However, applying a theory to make predictions in new settings is challenging: rarely can it be done without ad hoc modifications to account for setting-specific factors.
• We argue that AI agents put in simulations of those novel settings offer an alternative for applying theory, requiring minimal or no modifications.
• We present an approach for building such "general" agents that use theory-grounded natural language instructions, existing empirical data, and knowledge acquired by the underlying AI during training.