Measuring and Shaping Capabilities and Dispositions Under Multi-Agent Selection Pressures

Develop methods to measure and shape the capabilities and dispositions of AI systems that specifically account for multi-agent selection pressures, including how competitive and adaptive interactions select for traits that impact cooperation, deception, and conflict.

Background

Training and deployment in multi-agent environments can select for dispositions and capabilities distinct from those arising in single-agent settings. Competitive pressures, repeated interactions, and adaptive co-learning may favor traits such as deception, aggression, or collusion that elevate systemic risk.

Existing evaluations often overlook how agents change when interacting with diverse co-players and evolving environments. New measurement tools and training schemes are needed to characterize and steer these selection dynamics toward cooperative, reliable behavior.