mlsanityData to 14 Oct 2026

A Simulation System Towards Solving Societal-Scale Manipulation

arXiv 2410.13915 ↗First posted 17 Oct 2024cs.SI cs.AI cs.CYcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 28 of its 51 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

28 of 51 references.

References · 28

  1. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  2. 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 752
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  12. 2023On the steerability of large language models toward data-driven personascited by 53
16 more
  1. 2023Value FULCRA: Mapping Large Language Models to the Multidimensional Spectrum of Basic Human Valuescited by 50
  2. 2024Assessing Political Bias in Large Language Modelscited by 32
  3. 2024Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM argumentscited by 30
  4. 2022TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendationcited by 25
  5. 2021The Effect of People Recommenders on Echo Chambers and Polarizationcited by 20
  6. 2018Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Webcited by 18
  7. 2019Modeling echo chambers and polarization dynamics in social networkscited by 16
  8. 2024Are Large Language Models (LLMs) Good Social Predictors?cited by 13
  9. 2016Modeling confirmation bias and polarizationcited by 11
  10. 2024Whose Side Are You On? Investigating the Political Stance of Large Language Modelscited by 10
  11. 2021Capitol (Pat)riots: A comparative study of Twitter and Parlercited by 6
  12. 2015Echo chambers in the age of misinformationcited by 5
  13. 2018Algorithmic bias amplifies opinion polarization: A bounded confidence modelcited by 4
  14. 2019Opinion Dynamics with Backfire Effect and Biased Assimilationcited by 4
  15. 2021The effect of algorithmic bias and network structure on coexistence, consensus, and polarization of opinionscited by 3
  16. 2016The role of homophily in the emergence of opinion controversiescited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 9; 2026: 2.

12 papers.