How to build consensus without social influence?
Bayesian Optimization for Building Social-Influence-Free Consensus
This paper introduces Social Bayesian Optimization (SBO), a method for group decision-making that minimizes the influence of social dynamics (e.g., groupthink) on individual preferences. It uses a dual voting system: "public" votes (easily influenced) and "private" votes (truthful but expensive). SBO models social influence as a graph and learns this graph using the dual votes. This allows the system to rely primarily on the cheaper public votes while correcting for social influence.
For LLM-based multi-agent systems, SBO offers a framework for achieving truthful consensus by combining cheaper, readily available, but potentially biased interactions (analogous to public votes) with more costly, less readily available unbiased interactions (analogous to private votes). It highlights the importance of accounting for and mitigating social influence in multi-agent systems, particularly when using LLMs for preference elicitation and decision-making. The proposed graph-based approach to modeling social influence could be relevant for understanding and controlling interactions in LLM-driven multi-agent communication.