标题： 当机器相互遇见时：网络效应以及历史在多智能体AI中的战略作用 显示英文标题

标题： When Machines Meet Each Other: Network Effects and the Strategic Role of History in Multi-Agent AI

摘要： 随着人工智能（AI）进入代理时代，大型语言模型（LLMs）越来越多地被部署为自主代理，它们相互交互而非孤立运行。这种转变引发了一个基本问题：在相互依赖的环境中，机器代理的行为如何？在这种环境中，结果不仅取决于自身的决策，还取决于同伴的协调预期。为了解决这个问题，我们研究了在经典网络效应游戏中LLM代理的行为，经济学理论预测会收敛到一个实现预期均衡（FEE）。我们设计了一个实验框架，在其中，50个异构的基于GPT-5的代理在系统变化的网络效应强度、价格轨迹和决策历史长度下反复互动。结果表明，LLM代理系统性地偏离FEE：在低价格时低估参与度，在高价格时高估参与度，并且持续存在分散现象。关键的是，历史的结构被证明是一个设计杠杆。简单的单调历史——过去的结果呈现稳定上升或下降趋势——有助于稳定协调，而非单调历史则加剧偏离和路径依赖。个体层面的回归分析进一步表明，价格是偏差的主要驱动因素，历史缓和了这一影响，而网络效应放大了情境扭曲。这些发现通过提供在网络效应下多代理AI系统的第一个系统性证据，推进了机器行为研究，并为实际配置此类系统提供了指导。 显示英文摘要

摘要： As artificial intelligence (AI) enters the agentic era, large language models (LLMs) are increasingly deployed as autonomous agents that interact with one another rather than operate in isolation. This shift raises a fundamental question: how do machine agents behave in interdependent environments where outcomes depend not only on their own choices but also on the coordinated expectations of peers? To address this question, we study LLM agents in a canonical network-effect game, where economic theory predicts convergence to a fulfilled expectation equilibrium (FEE). We design an experimental framework in which 50 heterogeneous GPT-5-based agents repeatedly interact under systematically varied network-effect strengths, price trajectories, and decision-history lengths. The results reveal that LLM agents systematically diverge from FEE: they underestimate participation at low prices, overestimate at high prices, and sustain persistent dispersion. Crucially, the way history is structured emerges as a design lever. Simple monotonic histories-where past outcomes follow a steady upward or downward trend-help stabilize coordination, whereas nonmonotonic histories amplify divergence and path dependence. Regression analyses at the individual level further show that price is the dominant driver of deviation, history moderates this effect, and network effects amplify contextual distortions. Together, these findings advance machine behavior research by providing the first systematic evidence on multi-agent AI systems under network effects and offer guidance for configuring such systems in practice.