构建可模拟社会级操纵的仿真系统，研究虚假信息对选举的影响。
A Simulation System Towards Solving Societal-Scale Manipulation
- 基于Concordia框架，集成Mastodon实现线上社交互动模拟。
- 通过纵向调查追踪个体政治立场变化，验证党派操纵可改变选举结果。
- 适用于研究数字时代社会操纵机制与防御策略的研究者。
AI驱动的操纵行为对社会信任和民主进程构成重大威胁。然而，在真实世界中大规模研究此类影响在伦理和操作上均不现实，亟需可在受控环境中模拟这些动态的仿真工具，以测试潜在防御措施。本文提出一个仿真环境，完善了Concordia框架，通过集成Mastodon服务器，在离线现实活动基础上引入线上社交互动。改进了仿真效率与信息传播路径，并添加了包括纵向调查在内的测量工具。通过定制案例展示了如何追踪代理人的政治立场变化，证实党派操纵能显著影响选举结果。
原文摘要 · Abstract (English)
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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