Bayesian Persuasion

Bayesian persuasion studies how an informed party (sender) can strategically reveal information to influence the decisions of a less-informed party (receiver). Current research focuses on applying this framework to large language models (LLMs), using techniques like reinforcement learning and Bayesian optimization to design persuasive strategies, often incorporating user personality and behavioral models. This field is significant for understanding and mitigating the potential societal impact of persuasive AI systems, ranging from beneficial applications in health and finance to concerns about misinformation and manipulation. The development of benchmarks and algorithms for measuring and improving persuasiveness is a key area of ongoing investigation.