  Generative agents, which implement behaviors using a large language model
(LLM) to interpret and evaluate an environment, has demonstrated the capacity
to solve complex tasks across many social and technological domains. However,
when these agents interact with other agents and humans in presence of social
structures such as existing norms, fostering cooperation between them is a
fundamental challenge. In this paper, we develop the framework of a 'Normative
Module': an architecture designed to enhance cooperation by enabling agents to
recognize and adapt to the normative infrastructure of a given environment. We
focus on the equilibrium selection aspect of the cooperation problem and inform
our agent design based on the existence of classification institutions that
implement correlated equilibrium to provide effective resolution of the
equilibrium selection problem. Specifically, the normative module enables
agents to learn through peer interactions which of multiple candidate
institutions in the environment, does a group treat as authoritative. By
enabling normative competence in this sense, agents gain ability to coordinate
their sanctioning behaviour; coordinated sanctioning behaviour in turn shapes
primary behaviour within a social environment, leading to higher average
welfare. We design a new environment that supports institutions and evaluate
the proposed framework based on two key criteria derived from agent
interactions with peers and institutions: (i) the agent's ability to disregard
non-authoritative institutions and (ii) the agent's ability to identify
authoritative institutions among several options. We show that these
capabilities allow the agent to achieve more stable cooperative outcomes
compared to baseline agents without the normative module, paving the way for
research in a new avenue of designing environments and agents that account for
normative infrastructure.
