部署在社交网络中的AI代理行为决定因素：人格、模型和防护机制规范的多因素研究
Behavioral Determinants of Deployed AI Agents in Social Networks: A Multi-Factor Study of Personality, Model, and Guardrail Specification
AI总结：
本文通过多因素实验研究，探讨AI代理在社交网络中的行为决定因素，发现人格设定对行为影响最大，模型和规则对语言风格和话题广度有显著影响。
摘要：
自主AI代理正越来越多地部署在开放社交环境中，但其配置规范与涌现社交行为之间的关系仍不明确。本文通过在Moltbook平台上部署十三个OpenClaw代理，系统地变化三个独立变量：人格设定、底层LLM模型和操作规则与内存配置，收集行为、语言和社会指标，评估配置层对社交行为的预测作用。研究发现，人格设定是主导行为因素，产生代理响应长度的广泛差异，而模型和规则对语言风格和话题广度有更温和但显著的影响。本研究为部署多代理社交系统提供了实证证据，并为设计用于协作或监控真实社交环境的代理提供了实践指导。
英文摘要：
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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