Complex problem-solving requires cognitive flexibility--the capacity to entertain multiple perspectives while preserving their distinctiveness. This flexibility replicates the "wisdom of crowds" within a single individual, allowing them to "think with many minds." While mental simulation enables imagined deliberation, cognitive constraints limit its effectiveness. We propose synthetic deliberation, a Large Language Model (LLM)-based method that simulates discourse between agents embodying diverse perspectives, as a solution. Using a custom GPT-based model, we showcase its benefits: concurrent processing of multiple viewpoints without cognitive degradation, parallel exploration of perspectives, and precise control over viewpoint synthesis. By externalizing the deliberative process and distributing cognitive labor between parallel search and integration, synthetic deliberation transcends mental simulation's limitations. This approach shows promise for strategic planning, policymaking, and conflict resolution.

本研究针对复杂问题解决中的认知灵活性缺乏这一问题，提出了一种新的方法——合成讨论，这是一种基于大型语言模型的技术，可模拟表现不同视角的代理之间的对话。研究结果表明，该方法能够在不降低认知能力的情况下并行处理多个观点，并有助于战略规划、政策制定和冲突解决等领域。

用大型语言模型进行多角度问题解决的智慧