Multi-perspective reasoning for Social-AI agents

Develop models that enable Social-AI agents to perceive and reason over concurrent, interdependent perspectives of multiple actors during interactions; determine whether a single joint model or multiple actor-specific models more effectively represent social phenomena across an interaction; and design mechanisms to efficiently and accurately update agents’ perceptions of other actors’ perspectives during intermittent interactions over time.

Background

In social interactions, each actor maintains a subjective, evolving perspective influenced by roles, settings, norms, and attributes. These perspectives are interdependent, with each actor’s view affecting and being affected by others over time.

The authors highlight the need for modeling paradigms that capture this multi-perspective dynamics, spanning theory-of-mind and counterfactual social reasoning, and call for benchmarks in naturalistic settings to assess such capabilities.