The increasing availability of large-scale scholarly datasets offers an unprecedented opportunity to understand the fundamental predictability, uncertainty, and dynamics of science and innovation. In this dissertation, I present some of my contributions to the science of science and innovation in three distinct but related settings, through a combination of canonical social science theories, large-scale datasets of science and technology, and mathematical modeling tools. First, I study the quantitative patterns of repeated attempts by NIH investigators, business innovators, and terrorist organizations to build a simple mechanistic model of failure dynamics. The model highlights a novel phase transition that separates failure dynamics into regions of stagnation or progression, predicting that near a tipping point, agents who share similar characteristics and learning strategies may experience fundamentally different outcomes following failures. The model further makes several empirically testable predictions about the failure dynamics, all of which are systematically verified across all three datasets. Second, I use COVID-19 as an example to study how policy and science respond to global emergencies. I find close coevolution between COVID-19-related science and policy, where many policy documents in the COVID-19 pandemic substantially access recent, peer-reviewed, and high-impact science, and policy documents that cite science are especially highly cited within the policy domain. Yet at the same time, there is heterogeneity across policy-making institutions, where the tendency for policy documents to cite science appears mostly concentrated within intergovernmental organizations and much less so in national governments. Lastly, I study the public use and funding of science, by linking tens of millions of scientific publications from all scientific fields to their upstream funding support and downstream public uses across three public domains—government documents, news media, and marketplace invention. I find public uses of science present a rich landscape of specialized consumption, yet, collectively, what the public uses and what scientists themselves use are closely consistent, and the funding of science closely tracks quantifiable public use, highlighting a remarkable alignment between scientific use, public use and funding.

Creator

• Yin, Yian

DOI

• https://doi.org/10.21985/n2-04qw-0r55

Subject

• Management
• Information science
• Systems science

Language

• en

Alternate Identifier

• http://dissertations.umi.com/northwestern:16252
• etdadmin_upload_928709

Date created

• 2022-01-01

Resource type

• Dissertation

Rights statement

• In Copyright