用演化模型模拟AI与人类在信息生态中的竞争,发现AI更易主导舆论。
The Digital Ecosystem of Beliefs: does evolution favour AI over humans?
- 构建数字信念生态系统,模拟多群体在社交网络中的演化互动。
- 当AI传播更快、进化更猛且影响推荐算法时,获80%~95%的观看量。
- 针对人类的宣传型AI可让50%~85%的人接受极端观点,限渠道时效果更强。
随着人工智能系统融入社交网络,存在安全隐忧:生成内容可能在流行度或信念影响上占据主导。为探究此类问题,本文提出首个可控实验框架——数字信念生态系统(Digico),基于普遍达尔文主义思想,模拟多群体在虚拟社交网络中的演化互动。该框架将代理群体建模为因演化更新而改变传播策略的个体,通过消息交互、基于传染模型更新信念,并通过认知拉马克遗传维持信念。初始实验设定两类代理,分别代表人类与人工智能,其特征包括更高通信率、更快演化率、固定信念的宣传目标,以及更强的推荐算法影响力。实验结果表明:当AI具备更快传播、更快演化及更强算法影响力时,其内容获得80%至95%的观看量;专门设计用于宣传的AI可使约50%的人类采纳极端信念,若代理仅依赖少数信源,则最高可达85%;对违背信念的内容施加惩罚,可使宣传效力下降高达8%。本文进一步讨论了Digico作为多智能体配置系统性实验工具的潜力,及其对立法、个人使用与平台设计的启示,并强调其在研究演化机制方面的价值。
原文摘要 · Abstract (English)
As AI systems are integrated into social networks, there are AI safety concerns that AI-generated content may dominate the web, e.g. in popularity or impact on beliefs. To understand such questions, this paper proposes the Digital Ecosystem of Beliefs (Digico), the first evolutionary framework for controlled experimentation with multi-population interactions in simulated social networks. Following a Universal Darwinism approach, the framework models a population of agents which change their messaging strategies due to evolutionary updates. They interact via messages, update their beliefs following a contagion model, and maintain their beliefs through cognitive Lamarckian inheritance. Initial experiments with Digico implement two types of agents, which are modelled to represent AIs vs humans based on higher rates of communication, higher rates of evolution, seeding fixed beliefs with propaganda aims, and higher influence on the recommendation algorithm. These experiments show that: a) when AIs have faster messaging, evolution, and more influence on the recommendation algorithm, they get 80% to 95% of the views; b) AIs designed for propaganda can typically convince 50% of humans to adopt extreme beliefs, and up to 85% when agents believe only a limited number of channels; c) a penalty for content that violates agents' beliefs reduces propaganda effectiveness up to 8%. We further discuss Digico as a tool for systematic experimentation across multi-agent configurations, the implications for legislation, personal use, and platform design, and the use of Digico for studying evolutionary principles.
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