  Multi-agent AI systems can be used for simulating collective decision-making
in scientific and practical applications. They can also be used to introduce a
diverse group discussion step in chatbot pipelines, enhancing the cultural
sensitivity of the chatbot's responses. These applications, however, are
predicated on the ability of AI agents to reliably adopt assigned personas and
mimic human interactions. To evaluate the ability of LLM agents to satisfy
these requirements, we examine AI agent ensembles engaged in cultural
collaboration and debate by analyzing their private responses and chat
transcripts. Our findings suggest that multi-agent discussions can encourage
collective decisions that reflect diverse perspectives, yet this benefit is
tempered by the agents' susceptibility to conformity due to perceived peer
pressure and challenges in maintaining consistent personas and opinions.
Instructions that encourage debate in support of one's opinions rather than
collaboration increase the rate of inconstancy. Without addressing the factors
we identify, the full potential of multi-agent frameworks for producing more
culturally diverse AI outputs or more realistic simulations of group
decision-making will remain untapped.
