AI 中文总结
本文研究LLM智能体构建的人工社会，通过控制变量实验发现，五种市场制度的规则差异会显著改变市场结果，证明制度架构对人工社会模拟的重要影响。
AI 中文摘要
由大语言模型（LLM）智能体构建的人工社会正成为经济学、政治学、社会学和计算机科学领域的实用研究工具。多数研究关注智能体的属性：其提示词、角色设定、记忆、推理能力及与人类受试者的相似性。本文指出，模拟的制度架构同样重要。笔者通过一项小型重复诱导价值市场实验证明了这一点：相同的LLM智能体面临相同的私人价值、成本、历史和收益框架指令，仅在五种标准市场制度（ call market、posted-offer market、posted-bid market、continuous double auction、bilateral bargaining）的交易规则上存在差异，结果差异显著。call市场实现了88.6%的有效剩余；posted-offer和posted-bid市场实现约66%；连续双重拍卖实现71.5%；双边讨价还价实现56.4%。制度还会改变交易量、价格与竞争均衡的距离，以及买卖双方的剩余分配。这些结果表明，即便是微小的制度变化也能产生性质不同的人工社会结果。
英文摘要
Artificial societies built from large language model (LLM) agents are becoming a practical research tool in economics, political science, sociology, and computer science. Most attention has focused on the properties of the agents: their prompts, personas, memory, reasoning, and similarity to human subjects. This paper argues that the institutional architecture of a simulation is equally important. I demonstrate the point in a small repeated induced-value market experiment. The same LLM agents face the same private values, costs, history, and payoff-framed instructions, while only the rules of exchange vary across five standard market institutions: a call market, posted-offer market, posted-bid market, continuous double auction, and bilateral bargaining. Outcomes differ sharply. Call markets realize 88.6% of efficient surplus; posted-offer and posted-bid markets realize about 66%; continuous double auctions realize 71.5%; and bilateral bargaining realizes 56.4%. Institutions also change trade quantities, price distance from competitive equilibrium, and the division of surplus between buyers and sellers. These results show that even minimal institutional changes can generate qualitatively different artificial social outcomes.
Comments19 pages, 2 figures, 2 tables
