Over the past decades, cognitive neuroscientists and behavioral economists have recognized the value of describing the process of decision making in detail and modeling the emergence of decisions over time. For example, the time it takes to decide can reveal more about an agents true hidden preferences than only the decision itself. Similarly, data that track the ongoing decision process such as eye movements or neural recordings contain critical information that can be exploited, even if no decision is made. Here, we argue that artificial intelligence (AI) research would benefit from a stronger focus on insights about how decisions emerge over time and incorporate related process data to improve AI predictions in general and human-AI interactions in particular. First, we introduce a highly established computational framework that assumes decisions to emerge from the noisy accumulation of evidence, and we present related empirical work in psychology, neuroscience, and economics. Next, we discuss to what extent current approaches in multi-agent AI do or do not incorporate process data and models of decision making. Finally, we outline how a more principled inclusion of the evidence-accumulation framework into the training and use of AI can help to improve human-AI interactions in the future.

翻译：过去几十年间，认知神经科学家与行为经济学家充分认识到详细描述决策过程、并建模决策随时间动态涌现的价值。例如，决策时长不仅能揭示决策结果本身，更能反映决策者真实的潜在偏好。同样，追踪持续决策过程的数据（如眼动轨迹或神经记录）即便未产生最终决策，也蕴含着可资利用的关键信息。本文认为，人工智能研究若能更聚焦于"决策如何随时间涌现"这一洞见，并将相关过程数据融入其中，将能在整体上提升AI预测能力，尤其能优化人机交互表现。首先，我们引入一个高度成熟的认知计算框架——该框架假设决策源于证据的噪声累积过程，并系统梳理了心理学、神经科学与经济学领域的相关实证研究。其次，我们探讨当前多智能体AI方法在多大程度上采纳了决策过程数据与决策模型。最后，我们提出将证据累积框架更规范地融入AI训练与应用的路径，论证此举如何有助于未来人机交互的实质性提升。