解码人类因素：为战略预见的高保真行为预测
Decoding the Human Factor: High Fidelity Behavioral Prediction for Strategic Foresight
AI总结：
本文提出LBM模型，通过高维特质轮廓预测个体战略选择，提升高保真行为模拟能力，应用于战略预见等领域。
摘要：
在高风险环境中预测人类决策仍然是人工智能面临的核心挑战。尽管大型语言模型（LLMs）在一般推理方面表现强劲，但它们往往难以生成一致的、针对个体的行为，特别是在准确预测依赖于心理特质与情境约束之间复杂相互作用的情况下。基于提示的方法在此场景中表现脆弱，表现出身份漂移和有限的能力来利用越来越详细的人设描述。为了解决这些限制，我们引入了大型行为模型（LBM），这是一种经过微调以高保真度预测个体战略选择的行为基础模型。LBM从短暂的人设提示转向行为嵌入，通过条件于一个结构化、高维特质轮廓，该轮廓源自全面的心理测量电池。在一项专有数据集上进行训练，该数据集将稳定的倾向、动机状态和情境约束与观察到的选择联系起来，LBM学习将丰富的心理档案映射到多样化的战略困境中的离散行动。在一项保留场景评估中，LBM微调在行为预测方面优于未适应的Llama-3.1-8B-Instruct基础模型，并在条件于大五特质时与前沿基线表现相当。此外，我们发现虽然基于提示的基线表现出复杂性天花板，但LBM继续受益于越来越密集的特质轮廓，其性能随着额外特质维度的提供而提高。这些结果确立了LBM作为高保真行为模拟的可扩展方法，使在战略预见、谈判分析、认知安全和决策支持中的应用成为可能。
英文摘要：
Predicting human decision-making in high-stakes environments remains a central challenge for artificial intelligence. While large language models (LLMs) demonstrate strong general reasoning, they often struggle to generate consistent, individual-specific behavior, particularly when accurate prediction depends on complex interactions between psychological traits and situational constraints. Prompting-based approaches can be brittle in this setting, exhibiting identity drift and limited ability to leverage increasingly detailed persona descriptions. To address these limitations, we introduce the Large Behavioral Model (LBM), a behavioral foundation model fine-tuned to predict individual strategic choices with high fidelity. LBM shifts from transient persona prompting to behavioral embedding by conditioning on a structured, high-dimensional trait profile derived from a comprehensive psychometric battery. Trained on a proprietary dataset linking stable dispositions, motivational states, and situational constraints to observed choices, LBM learns to map rich psychological profiles to discrete actions across diverse strategic dilemmas. In a held-out scenario evaluation, LBM fine-tuning improves behavioral prediction relative to the unadapted Llama-3.1-8B-Instruct backbone and performs comparably to frontier baselines when conditioned on Big Five traits. Moreover, we find that while prompting-based baselines exhibit a complexity ceiling, LBM continues to benefit from increasingly dense trait profiles, with performance improving as additional trait dimensions are provided. Together, these results establish LBM as a scalable approach for high-fidelity behavioral simulation, enabling applications in strategic foresight, negotiation analysis, cognitive security, and decision support.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
