Physicalization of Social Experiments: Harnessing Large Language Models for Automated Exploration of Emergent Behaviors in Simulated Social Systems

Two significant impediments to success of the social sciences in comparison to physics are the inherent difficulty in both rapidly executing multiple controlled experiments to explore a parameter space and determining what parameter space to explore. In this work, we present a computational framework and platform that simulates the entire social scientific process, leveraging Large Language Models (LLMs) to study human actors within social systems. We create controlled environments, akin to toy models in physics, that systematically explore the space parameter of variables relevant to any social system (such as attributes of human actors), allowing for the exponentially faster discovery of emergent social behaviors as compared to traditional social science experimentation. Central to our approach is the automatic generation of Structural Causal Models (SCMs) that generate statistical correlations of potential interactions within a social system and outline the requisite metrics and tools to observe and measure these nonlinear dynamics. With the flexibility to vary controlled variables across a nearly infinite parameter space, our system offers a sandbox to simulate and analyze various social scenarios – from wage bargaining and auction mechanics to nuclear weapon negotiations. Our framework and platform offers a new playground for physicists to study the nonlinear dynamics and emergent phenomena in human social systems.