Dynamic Decision Making: Learning Processes and New Research Directions

OBJECTIVE The aim of this manuscript is to provide a review of contemporary research and applications on dynamic decision making (DDM). BACKGROUND Since early DDM studies, there has been little systematic progress in understanding decision making in complex, dynamic systems. Our review contributes to better understanding of decision making processes in dynamic tasks. METHOD We discuss new research directions in DDM to highlight the value of simplification in the study of complex decision processes, divided into experimental and theoretical/computational approaches, and focus on problems involving control tasks and search-and-choice tasks. In computational modeling, we discuss recent developments in instance-based learning and reinforcement learning that advance modeling the processes of dynamic decisions. RESULTS Results from DDM research reflect a trend to scale down the complexity of DDM tasks to facilitate the study of the process of decision making. Recent research focuses on the dynamic complexity emerging from the interactions of actions and outcomes over time even in simple dynamic tasks. CONCLUSION The study of DDM in theory and practice continues to be a priority area of research. New research directions can help the human factors community to understand the effects of experience, knowledge, and adaption processes in DDM tasks, but research challenges remain to be addressed, and the recent perspectives discussed can help advance a systematic DDM research program. APPLICATION Classical domains, such as automated pilot systems, fighting fires, and medical emergencies, continue to be central applications of basic DDM research, but new domains, such as cybersecurity, climate change, and forensic science, are emerging as other important applications.