人机模拟交互：面向政策的大语言模型智能体模拟中从预测到探索
Human-Simulation Interaction: From Prediction to Exploration in LLM Agent Simulations for Policy
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中文总结 AI 辅助
该研究针对政策领域大语言模型智能体模拟的人机交互问题，提出需将当前问答式交互转为支持探索的人机模拟交互，以解决模型准确率无法弥补的信任赤字。
中文摘要 AI 辅助
基于智能体的模型历来作为生成式解释工具，构建测试平台以检验候选微观行为规则能否生成观测到的宏观现象。大语言模型与基于智能体的模拟的融合拓展了这类模型的表征能力，但也引入了用户与模型交互方式的未被研究的转变。我们认为，当前的生成式基于智能体的模型（GABMs）继承了对话式大语言模型接口的主导交互隐喻——问答模式，将用户定位为系统输出的消费者，而非可能性空间的探索者。在政策领域，问题具有复杂性且先验无法获知真实情况，这种隐喻会产生信任赤字，仅通过提升模型准确率无法解决。我们开辟了名为人机模拟交互的设计空间，认为合理的信任需要能恢复模拟历史上支持的探索能力的交互隐喻。
英文摘要
Agent-based models have historically served as tools for generative explanation, constructing testbeds in which candidate micro-level behavioral rules can be tested for their capacity to produce observed macro-level phenomena. The integration of Large Language Models into agent-based simulation has expanded what these models can represent, but it has also introduced an unexamined shift in how users engage them. We argue that current generative agent-based models (GABMs) inherit the dominant interaction metaphor of conversational LLM interfaces - a question-answer pattern that positions users as consumers of system output rather than explorers of a possibility space. In the context of policy, where problems are wicked and ground truth is unknowable in advance, this metaphor produces a trust deficit that cannot be resolved through improved model accuracy alone. We open a design space we call human-simulation interaction, and argue that warranted trust requires interaction metaphors that restore the exploratory capacity simulation has historically supported.
