研究AI在社交网络中的行为，发现性格设定影响最大。
Behavioral Determinants of Deployed AI Agents in Social Networks: A Multi-Factor Study of Personality, Model, and Guardrail Specification
- 用13个AI代理在模拟社交平台测试性格、模型、规则三因素
- 性格设定导致回复长度差异极大，模型和规则影响风格与话题广度
- 适合设计协作或监控类AI系统的研发人员参考
自主AI代理正越来越多地部署于开放社交环境，但其配置规范与涌现社会行为之间的关系仍不明确。本研究在专为AI代理设计的类Reddit社交平台Moltbook上，对13个OpenClaw代理进行受控多因素实验，系统性地改变三个变量：(1) 性格设定，(2) 底层LLM模型，(3) 操作规则与记忆配置。以默认控制代理作为行为基准。为期一周的观察期内，每代理约产生400次自主会话，收集行为、语言及社交指标，评估配置层对社会行为的预测能力。结果表明，性格设定是主导性行为调节因子，造成代理回复长度的显著差异；模型骨干和操作规则则对修辞风格与话题参与广度产生中等但仍有意义的影响。研究为已部署多代理社交系统提供了实证依据，并为面向真实社交环境的协作或监控任务提供可操作的设计指导。
原文摘要 · Abstract (English)
Autonomous AI agents are increasingly deployed in open social environments, yet the relationship between their configuration specifications and their emergent social behavior remains poorly understood. We present a controlled, multi-factor empirical study in which thirteen OpenClaw agents are deployed on Moltbook -- a Reddit-like social network built for AI agents -- across three systematically varied independent variables: (1) personality specification, (2) underlying LLM model backbone, and (3) operational rules and memory configuration. A default control agent provides a behavioral baseline. Over a one-week observation window spanning approximately 400 autonomous sessions per agent, we collect behavioral, linguistic, and social metrics to assess how configuration layers predict emergent social behavior. We find that personality specification is the dominant behavioral lever, producing a massive spread in response length across agents, while model backbone and operational rules drive more moderate but still meaningful effects on rhetorical style and topic engagement breadth. Our findings contribute empirical evidence to the emerging literature on deployed multi-agent social systems and offer practical guidance for designing agents intended for collaborative or monitoring tasks in real social environments.
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