How AI Agents Follow the Herd of AI? Network Effects, History, and Machine Optimism

• URL: http://arxiv.org/abs/2512.11943v1
• Date: Fri, 12 Dec 2025 12:14:48 GMT
• Title: How AI Agents Follow the Herd of AI? Network Effects, History, and Machine Optimism
• Authors: Yu Liu, Wenwen Li, Yifan Dou, Guangnan Ye,
• Abstract summary: This study investigates how AI agents navigate network-effect games, where individual payoffs depend on peer participatio--a context underexplored in multi-agent systems.<n>We introduce a novel workflow design using large language model (LLM)-based agents in repeated decision-making scenarios.
• Score: 7.1683021355290295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding decision-making in multi-AI-agent frameworks is crucial for analyzing strategic interactions in network-effect-driven contexts. This study investigates how AI agents navigate network-effect games, where individual payoffs depend on peer participatio--a context underexplored in multi-agent systems despite its real-world prevalence. We introduce a novel workflow design using large language model (LLM)-based agents in repeated decision-making scenarios, systematically manipulating price trajectories (fixed, ascending, descending, random) and network-effect strength. Our key findings include: First, without historical data, agents fail to infer equilibrium. Second, ordered historical sequences (e.g., escalating prices) enable partial convergence under weak network effects but strong effects trigger persistent "AI optimism"--agents overestimate participation despite contradictory evidence. Third, randomized history disrupts convergence entirely, demonstrating that temporal coherence in data shapes LLMs' reasoning, unlike humans. These results highlight a paradigm shift: in AI-mediated systems, equilibrium outcomes depend not just on incentives, but on how history is curated, which is impossible for human.

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