  Strategic social deduction games serve as valuable testbeds for evaluating
the understanding and inference skills of language models, offering crucial
insights into social science, artificial intelligence, and strategic gaming.
This paper focuses on creating proxies of human behavior in simulated
environments, with Among Us utilized as a tool for studying simulated human
behavior. The study introduces a text-based game environment, named
AmongAgents, that mirrors the dynamics of Among Us. Players act as crew members
aboard a spaceship, tasked with identifying impostors who are sabotaging the
ship and eliminating the crew. Within this environment, the behavior of
simulated language agents is analyzed. The experiments involve diverse game
sequences featuring different configurations of Crewmates and Impostor
personality archetypes. Our work demonstrates that state-of-the-art large
language models (LLMs) can effectively grasp the game rules and make decisions
based on the current context. This work aims to promote further exploration of
LLMs in goal-oriented games with incomplete information and complex action
spaces, as these settings offer valuable opportunities to assess language model
performance in socially driven scenarios.
