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arxiv: 2410.13915 · v1 · pith:7S3VIQY2 · submitted 2024-10-17 · cs.SI · cs.AI· cs.CY

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
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Tom Gibbs Andreea Musulan Camille Thibault Busra Tugce Gurbuz Reihaneh Rabbany Jean-Fran\c{c}ois Godbout Kellin Pelrine
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classification cs.SI cs.AIcs.CY
keywords simulationmanipulationagentssettingstoolsactivityaddingaddress
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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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