The evolution of Large Language Models (LLMs) has introduced a new paradigm for investigating human behavior emulation. Recent research has employed LLM-based Agents to create a sociological research environment, in which agents exhibit behavior based on the unfiltered characteristics of large language models. However, these studies overlook the iterative development within a human-like setting - Human preferences and personalities are complex, shaped by various factors and subject to ongoing change as a result of environmental and subjective influences. In light of this observation, we propose Agent Framework for Shaping Preference and Personality (AFSPP), exploring the multifaceted impact of social networks and subjective consciousness on LLM-based Agents' preference and personality formation. With AFSPP, we have, for the first time, successfully replicated several key findings from human personality experiments. And other AFSPP-based experimental results indicate that plan making, sensory perceptions and social networking with subjective information, wield the most pronounced influence on preference shaping. AFSPP can significantly enhance the efficiency and scope of psychological experiments, while yielding valuable insights for Trustworthy Artificial Intelligence research for strategies to prevent undesirable preference and personality development.

翻译：大语言模型（LLM）的演进为人类行为模拟研究引入了新范式。近期研究借助基于LLM的智能体构建社会学研究环境，使智能体依据大语言模型未经筛选的特性展现行为。然而，这些研究忽视了类人情境中的渐进式发展过程——人类偏好与人格具有复杂性，受多种因素影响，并因环境和主观作用而持续演变。基于此发现，我们提出"塑造偏好与人格的智能体框架"（AFSPP），探究社交网络与主观意识对基于LLM智能体的偏好及人格形成的多维影响。借助AFSPP，我们首次成功复现了人类人格实验的多项关键发现。其他基于AFSPP的实验结果表明，计划制定、感官感知及含主观信息的社交互动对偏好塑造具有最显著的影响力。AFSPP能显著提升心理实验的效率与范畴，同时为可信人工智能研究中防范不良偏好与人格发展的策略提供重要见解。