The desire to predict discoveries -- to have some idea, in advance, of what will be discovered, by whom, when, and where -- pervades nearly all aspects of modern science. Individual scientists routinely make predictions about which research questions or topics are interesting, impactful, and fundable. Publishers and funding agencies evaluate manuscripts and project proposals in part by predicting their future impact. Faculty hiring committees make predictions about which candidates will make important scientific contributions over their career. And predictions are important to the public, who fund the majority of all scientific research through tax dollars. The more predictable we can make the process of facilitating scientific discovery, the more efficiently those resources can be used to support worthwhile technological, biomedical, and scientific advances.

Despite this pervasive need, our understanding of how individual discoveries or new areas of research emerge from existing research communities is limited, and relatively few predictions, by individuals, publishers, funders, or hiring committees, are made in a scientific way. How, then, can we know what is predictable and what is not? How can we intervene in this complex system to diversify or accelerate discovery? How do different types of inequalities limit or drive scholarship? Inequalities in science, whether social or epistemic, are pervasive and persistent. But whether and how much these inequalities in outcomes reflect true differences in merit, i.e., in the quality of ideas, or instead reflect systemic biases, either individual or structural, is a matter of intense debate. Formulating evidence-based policies that address and mitigate counter-productive inequalities, i.e., inequalities that limit general scientific progress, requires first understanding the causal structures that drive them, within a complex scientific ecosystem of interacting individuals and organizations.

Although scholarly work on the "science of science" reaches back many decades, the field has recently experienced a rapid expansion that has brought many new ideas and new researchers into it and broad disagreements about how best to construct causal theories that can address social and epistemic inequalities that may limit the production of knowledge. The goal of this workshop was to bring together a diverse set of experts in the science of science to develop a new synthesis of existing knowledge and overarching scientific questions of broad importance, in light of new data sources, new experiments, and new theoretical ideas about the social processes that lead to scientific discovery.

This workshop brought together 40 experts from a diverse set of scientific communities, spanning scientometrics, sociology, computer science, physics, ecology, statistics, economics, and political science, who (1) presented and synthesized recent fundamental advances in quantitative analysis and modeling of the structure and dynamics of the scientific ecosystem, its pervasive inequalities, and their relationship to scientific discovery, and (2) formulated a preliminary new synthesis of the science of science to help align different research communities' efforts toward broadly important questions about the causes and consequences of social and epistemic inequalities in the production of knowledge, and to identify critical data and computational needs to achieve these scientific goals. In addition, the workshop has led to several new cross-disciplinary research collaborations and new research projects.

Last Modified: 05/18/2023
Modified by: Aaron Clauset