A Simulation System Towards Solving Societal-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. We improve simulation efficiency and information flow, and add a set of measurement tools, particularly longitudinal surveys. We demonstrate the simulator with a tailored example in which we track agents' political positions and show how partisan manipulation of agents can affect election results.
Cite
@article{arxiv.2410.13915,
title = {A Simulation System Towards Solving Societal-Scale Manipulation},
author = {Maximilian Puelma Touzel and Sneheel Sarangi and Austin Welch and Gayatri Krishnakumar and Dan Zhao and Zachary Yang and Hao Yu and Ethan Kosak-Hine and Tom Gibbs and Andreea Musulan and Camille Thibault and Busra Tugce Gurbuz and Reihaneh Rabbany and Jean-François Godbout and Kellin Pelrine},
journal= {arXiv preprint arXiv:2410.13915},
year = {2024}
}