ABSTRACT

The era of big data offers researchers an unprecedented opportunity to test and refine scientific theories and solve societal challenges. Key challenges for the next generation include improving education, healthcare, and lowering the damaging impacts of human activities on the environment. All of these challenges require massive interdisciplinary collaborations and complementary large-scale data collection efforts. The staggering amounts of data being collected and analyzed today, however, are inherently limited in their utility because that data was not designed to meet specific research goals. To truly advance scientific theories in the field and address societal challenges requires engineering new data from the ground-up. This paper outlines the co-evolutionary approach to economics and data that is “economic data engineering.” It is organized around two basic constructs: beliefs and preferences and it illustrates how data engineering crosses boundaries between disciplines. The Kavli HUMAN Project represents the vanguard of engineering data for broad-based scientific and public policy research, enabling the discovery of causal relationships between biology, behavior, the environment, and society at large.