A learning agent that acquires social norms from public sanctions in decentralized multi-agent settings

Understand

Society is characterized by the presence of a variety of social norms: collective patterns of sanctioning that can prevent miscoordination and free-riding.

• Inspired by this, we aim to construct learning dynamics where potentially beneficial social norms can emerge.
• Since social norms are underpinned by sanctioning, we introduce a training regime where agents can access all sanctioning events but learning is otherwise decentralized.
• This setting is technologically interesting because sanctioning events may be the only available public signal in decentralized multi-agent systems where reward or policy-sharing is infeasible or undesirable.