SciSIP: Knowledge Networks and the Dynamics of Innovation

University Of Southern California, Los Angeles CA

Investigators

Abstract

The central tenet of this project is that knowledge discovery and valuation is a human activity, and as such, it is subject to cognitive heuristics and biases, or mental shortcuts that help people make quick decisions. Social and economic progress is driven by scientific discoveries, and technological and legal innovations. However, as new knowledge accumulates at an accelerating pace through the publication of new scientific papers, patent applications and legal opinions, it becomes ever more difficult to identify information that is critical for innovation. In order to create tools to speed up innovation, we first need to understand how people find and evaluate knowledge. This project will analyze patterns of citation networks in three domains -- physics papers, patents and federal court decisions -- to learn how scholars and innovators discover and evaluate knowledge. Behavioral data for studying cognitive heuristics is available in the form of citation networks. These networks capture the decisions that scholars and innovators made about what relevant documents to reference in their own work. By conducting comparative empirical analysis of citations made by physics papers, patents, and federal court decisions, this project will identify the strategies people use to decide what information to attend to, especially under conditions of information overload, and study the interplay between these strategies, the quality of information, and the decisions of others. In addition to its contributions to understanding how ideas are discovered and evaluated, this project will make a novel, clean data set of legal citations publicly available. The insights produced by this research will lead to new, robust measures of information quality and impact. The new understanding of the role of cognitive heuristics in citation will inform the design of next generation knowledge discovery tools that will help people to more optimally leverage citations to improve the efficiency and robustness of discovery and innovation.