Thinking with Many Minds: Using Large Language Models for Multi-Perspective Problem-Solving
This addresses the need for enhanced cognitive flexibility in strategic planning and policymaking, though it appears incremental as an application of existing LLMs to a known bottleneck.
The paper tackles the problem of cognitive limitations in multi-perspective problem-solving by proposing synthetic deliberation, an LLM-based method that simulates discourse between diverse agents, resulting in benefits like concurrent viewpoint processing and precise synthesis control.
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.