On the Principles behind Opinion Dynamics in Multi-Agent Systems of Large Language Models

We study the evolution of opinions inside a population of interacting large language models (LLMs). Every LLM needs to decide how much funding to allocate to an item with three initial possibilities: full, partial, or no funding. We identify biases that drive the exchange of opinions based on the LLM's tendency to (i) find consensus with the other LLM's opinion, (ii) display caution when specifying funding, and (iii) consider ethical concerns in its opinion. We find these biases are affected by the perceived absence of compelling reasons for opinion change, the perceived willingness to engage in discussion, and the distribution of allocation values. Moreover, tensions among biases can lead to the survival of funding for items with negative connotations. We also find that the final distribution of full, partial, and no funding opinions is more diverse when an LLM freely forms its opinion after an interaction than when its opinion is a multiple-choice selection among the three allocation options. In the latter case, consensus or polarization is generally attained. When agents are aware of past opinions, they seek to maintain consistency with them, and more diverse updating rules emerge. Our study is performed using a Llama 3 LLM.