Simulation System Towards Solving Societal-Scale Manipulation

Maximilian Puelma Touzel, Sneheel Sarangi, Austin Welch, Gayatri K, Dan Zhao, Zachary Yang, Hao Yu, Tom Gibbs, Ethan Kosak-Hine, Andreea Musulan, Camille Thibault, Busra Tugce Gurbuz, Reihaneh Rabbany, Jean-François Godbout, Kellin Pelrine

Keywords: simulation, manipulation, disinformation, ai safety, generative agents, llm

TL;DR: LLM-based multi-agent simulations to bypass experimental barriers to robust defenses against large-scale manipulation.

Abstract: The rise of AI-driven manipulation poses significant risks to societal trust and democratic processes. Yet, studying these effects in real-world settings at scale is ethically and logistically impractical, highlighting a need for simulation tools that can model these dynamics in controlled settings to enable experimentation with possible defenses. We present a simulation environment designed to address this. We elaborate upon the Concordia framework that simulates offline, `real life' activity by adding online interactions to the simulation through social media with the integration of a Mastodon server. Through a variety of means we then improve simulation efficiency and information flow, and add a set of measurement tools, particularly longitudinal surveys of the agents' political positions. We demonstrate the simulator with a tailored example of how partisan manipulation of agents can affect election results.

Submission Number: 115