Large Language Models (LLMs) have demonstrated their capabilities across various tasks, from language translation to complex reasoning. Understanding and predicting human behavior and biases are crucial for artificial intelligence (AI) assisted systems to provide useful assistance, yet it remains an open question whether these models can achieve this. This paper addresses this gap by leveraging the reasoning and generative capabilities of the LLMs to predict human behavior in two sequential decision-making tasks. These tasks involve balancing between exploitative and exploratory actions and handling delayed feedback, both essential for simulating real-life decision processes. We compare the performance of LLMs with a cognitive instance-based learning (IBL) model, which imitates human experiential decision-making. Our findings indicate that LLMs excel at rapidly incorporating feedback to enhance prediction accuracy. In contrast, the cognitive IBL model better accounts for human exploratory behaviors and effectively captures loss aversion bias, i.e., the tendency to choose a sub-optimal goal with fewer step-cost penalties rather than exploring to find the optimal choice, even with limited experience. The results highlight the benefits of integrating LLMs with cognitive architectures, suggesting that this synergy could enhance the modeling and understanding of complex human decision-making patterns.

翻译：大语言模型（LLMs）已在从语言翻译到复杂推理的多种任务中展现出其能力。理解并预测人类行为及偏差对于人工智能（AI）辅助系统提供有效帮助至关重要，然而这些模型能否实现此目标仍是一个开放性问题。本文通过利用LLMs的推理与生成能力来预测人类在两项序贯决策任务中的行为，以填补这一研究空白。这些任务涉及在利用性行动与探索性行动之间取得平衡，以及处理延迟反馈，两者对于模拟现实决策过程都至关重要。我们将LLMs的表现与一种模仿人类经验式决策的认知实例学习（IBL）模型进行了比较。我们的研究结果表明，LLMs擅长快速整合反馈以提高预测准确性。相比之下，认知IBL模型能更好地解释人类的探索行为，并有效捕捉损失厌恶偏差，即倾向于选择次优目标（因其步骤成本惩罚较少）而非通过探索寻找最优选择，即使在经验有限的情况下也是如此。这些结果凸显了将LLMs与认知架构相结合的优势，表明这种协同作用可以增强对复杂人类决策模式的建模与理解。