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arXiv · 2410.13915

A Simulation System Towards Solving Societal-Scale Manipulation

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

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Maximilian Puelma Touzel, Sneheel Sarangi, Austin Welch, Gayatri Krishnakumar, Dan Zhao, Zachary Yang, Hao Yu, Ethan Kosak-Hine, Tom Gibbs, Andreea Musulan, Camille Thibault, Busra Tugce Gurbuz, Reihaneh Rabbany, Jean-François Godbout, Kellin Pelrine. 2024-10-17. A Simulation System Towards Solving Societal-Scale Manipulation. https://arxiv.org/abs/2410.13915

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