Humans make decisions in dynamic environments (increasingly complex, highly uncertain, and changing situations) by searching for potential alternatives sequentially over time, to determine the best option at a precise moment. Surprisingly, the field of behavioral decision making has little to offer in terms of theoretical principles and practical guidelines on how people make decisions in dynamic situations. My research program aims to fill in this gap by developing theoretical understandings of decision processes as well as practical demonstrations of how these theoretical developments can improve human dynamic decision making. Throughout my research career, I have helped create, test, and improve a general theory of dynamic decision making, instance-based learning theory, IBLT. The methods I have used to contribute to IBLT are (1) laboratory experiments that rely on dynamic games in which humans make choices over time and space, individually and in teams, and from which we extrapolate robust phenomena and behavioral insights; and (2) computational, actionable cognitive models, which specify the decision-making process and the cognitive mechanisms involved into a computational algorithm. The combination of these methods spawned novel applications in areas such as cybersecurity, phishing, climate change, and human–machine interactions. In this paper, I will take you through my own intellectual exploratory experience of computational modeling of human decision processes, and how the integration of experimental work and cognitive modeling helped in discovering and uncovering the field of dynamic decision making.

中文翻译：

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学习和动态决策

人类在动态环境（日益复杂、高度不确定和不断变化的情况）中通过随时间顺序搜索潜在的替代方案来做出决策，以便在精确的时刻确定最佳选择。令人惊讶的是，行为决策领域几乎没有提供关于人们如何在动态情况下做出决策的理论原则和实践指南。我的研究计划旨在通过发展对决策过程的理论理解以及这些理论发展如何改善人类动态决策的实际演示来填补这一空白。在我的研究生涯中，我帮助创建、测试和改进了动态决策的一般理论、基于实例的学习理论，IBLT。我用来为 IBLT 做出贡献的方法是 (1) 实验室实验，这些实验依赖于人类在时间和空间上、个人和团队中做出选择的动态游戏，并从中推断出强大的现象和行为洞察力；(2) 计算的、可操作的认知模型，它指定了计算算法中涉及的决策过程和认知机制。这些方法的结合催生了在网络安全、网络钓鱼、气候变化和人机交互等领域的新应用。在本文中，我将带您了解我自己对人类决策过程计算建模的智力探索经验，以及实验工作和认知建模的整合如何帮助发现和揭示动态决策领域。