Collaborative Research: Building Empirically Informed Models of Scientific Search and Progress

Scientific progress is vital for advancing technology, fostering social development, and tackling global issues. Research indicates that innovative activity in science is slowing. The causes behind this decline are unknown.

Addressing this issue is hindered by our limited ability to empirically test how individual scientists make breakthrough discoveries. The researchers draw on the interdisciplinary expertise of a team with deep knowledge in sociology, agent-based modeling, and philosophy of science. By combining both theoretical and empirical studies, this award helps science to further understand the causes of, and solutions to, our declining rate of innovation.

By providing a deeper understanding of the factors that drive or hinder innovation, the award informs policies aimed at fostering a more conducive environment for scientific breakthroughs.

The supported research uses a novel mixed-method approach, where empirically informed agent-based simulation models are combined with empirical analysis of the scientific record. This allows for a comparison of both internal factors---those related to the structure and dynamics of scientific content---and external factors that operate outside and influence the body of scientific knowledge.

Internal factors involve the proliferation of scientific knowledge, while external factors encompass funding incentives, pressure to publish, and the challenges of remote collaboration. To increase innovation, it is essential to implement actionable policies that mitigate these factors and guide scientists in their contributions to scientific knowledge.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.