Optimizing auxiliary agent attributes beyond exogenous variables in LLM-powered simulations

Develop an optimization framework for selecting and endowing additional agent attributes—such as demographics, personalities, and other traits—beyond the structural causal model’s exogenous variables for large language model–powered agents, so as to improve simulation fidelity while avoiding redundancy and unintended interactions.

Background

The authors adopt a minimalist approach to agent construction, endowing LLM-powered agents primarily with goals, constraints, roles, names, and the exogenous variables required by the structural causal model. They note that additional attributes (e.g., demographics, personality traits) might enhance fidelity but could introduce redundancy or unforeseen interactions.

They explicitly state that optimizing which auxiliary attributes to include is unclear, motivating the need for a principled method to balance realism and experimental control in automated social-science simulations with LLM agents.