用动态干预让多智能体模拟既守规则又涌现真实社会行为
AgentDynEx: Nudging the Mechanics and Dynamics of Multi-Agent Simulations
- 引入里程碑与约束条件，确保模拟过程符合设定机制
- 动态干预使模拟推进更远，同时保持显著社会动态
- 适合研究社会行为、人机交互的学者与应用开发者
多智能体大语言模型模拟具有建模复杂人类行为与互动的潜力。若机制设置得当，可涌现出意想不到且有价值的社交动态。然而，如何在保持丰富涌现性的同时持续遵循模拟机制仍具挑战。本文提出AgentDynEx，一个帮助构建、追踪与修复模拟的AI系统。该系统引入里程碑作为检查点，失败条件作为护栏，确保模拟进程中的机制被遵守且动态有意义。同时提出‘引导’（nudging）方法，系统动态评估进展并在偏离预期时温和介入。技术评估显示，使用引导的模拟能更深入推进，且未降低显著动态的出现频率。案例研究中，真实用户利用AgentDynEx成功模拟了真实生活经历。本文强调引导作为引导智能体实现理想行为、同时保留自主性的关键手段。
原文摘要 · Abstract (English)
Multi-agent large language model simulations have the potential to model complex human behaviors and interactions. If the mechanics are set up properly, unanticipated and valuable social dynamics can surface. However, it is challenging to consistently enforce simulation mechanics while still allowing for rich and emergent dynamics. We present AgentDynEx, an AI system that helps set up, track, and repair simulations. Specifically, AgentDynEx introduces milestones that act as checkpoints and failure conditions that act as guardrails to ensure dynamics are relevant and mechanics are respected as the simulation progresses. It also introduces a method called nudging, where the system dynamically reflects on simulation progress and gently intervenes if it begins to deviate from intended outcomes. A technical evaluation found that nudging enables simulations to progress further without reducing the presence notable dynamics compared to simulations without nudging. A case study with AgentDynEx documented instances where real users were able to simulate lived experiences. We discuss the importance of nudging as a technique for guiding agents towards desirable behaviors while preserving their freedom of choice.
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