Data Engineering for Cognitive Economics
提出通过数据工程引入新数据形式，以分离识别偏好、信念等模型构念，应用于财富积累、消费、人机交互等领域，并倡导跨学科推广。
Cognitive economics studies imperfect information and decision-making mistakes. A central scientific challenge is that these can’t be identified in standard choice data. Overcoming this challenge calls for data engineering, in which new data forms are introduced to separately identify preferences, beliefs, and other model constructs. I present applications to traditional areas of economic research, such as wealth accumulation, earnings, and consumer spending. I also present less traditional applications to assessment of decision-making skills, and to human–AI interactions. Methods apply both to individual and to collective decisions. I make the case for broader application of data engineering beyond cognitive economics. It allows symbiotic advances in modeling and measurement. It cuts across existing boundaries between disciplines and styles of research.