AFSPP: Agent Framework for Shaping Preference and Personality with Large
  Language Models
Zihong He Changwang Zhang
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.