A Simulation System Towards Solving Societal-Scale Manipulation

TL;DR

This paper introduces a simulation system that models societal-scale manipulation via social media to study its effects and test defenses in a controlled environment, addressing ethical and logistical challenges.

Contribution

The paper presents the Concordia simulation framework that integrates social media interactions, improves efficiency, and adds measurement tools for studying manipulation effects.

Findings

Simulation can track political position changes over time.

Partisan manipulation influences election outcomes.

Enhanced measurement tools enable detailed analysis.

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.