{"ID":2829830,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.11943","arxiv_id":"2512.11943","title":"How AI Agents Follow the Herd of AI? Network Effects, History, and Machine Optimism","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.","short_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...","url_abs":"https://arxiv.org/abs/2512.11943","url_pdf":"https://arxiv.org/pdf/2512.11943v1","authors":"[\"Yu Liu\",\"Wenwen Li\",\"Yifan Dou\",\"Guangnan Ye\"]","published":"2025-12-12T12:14:48Z","proceeding":"cs.MA","tasks":"[\"cs.MA\",\"cs.AI\",\"econ.GN\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}