James A. Evans

About

James A. Evans is a professor at the University of Chicago, serving as the Director of the Knowledge Lab and the Faculty Director of the Masters Program in Computational Social Science. His research focuses on the collective system of thinking and knowing, exploring how attention, intuition, ideas, and shared reasoning develop and influence processes of agreement, doubt, and understanding. He is particularly interested in innovation, examining how new ideas and practices emerge and the role social and technical institutions such as the Internet, markets, and collaborations play in collective cognition and discovery. Evans's work has concentrated on modern science and technology, but he also investigates other domains of knowledge including news, law, religion, gossip, hunches, and modes of machine and historical thinking. He supports the creation of novel observatories for human understanding and action through crowd sourcing, information extraction from text and images, and distributed sensors. His methodological approaches include machine learning, generative modeling, and social and semantic network representations to explore knowledge processes, scale interpretive and field methods, and develop alternatives to current discovery regimes. His research has been supported by prominent agencies such as the NSF, NIH, and the Air Force Office of Science Research, and has been published in leading journals and featured in major media outlets. Evans also founded and directs the…

Research topics

• Computer Science
• Sociology
• Artificial Intelligence
• Social Science
• Political Science
• Data science
• Mathematics
• Epistemology
• Biology
• Knowledge management

Selected publications

• Slowed canonical progress in large fields of science

  Proceedings of the National Academy of Sciences · 2021 · 306 citations

  Senior authorCorresponding

  In many academic fields, the number of papers published each year has increased significantly over time. Policy measures aim to increase the quantity of scientists, research funding, and scientific output, which is measured by the number of papers produced. These quantitative metrics determine the career trajectories of scholars and evaluations of academic departments, institutions, and nations. Whether and how these increases in the numbers of scientists and papers translate into advances in kn…

  DOI

• Flat teams drive scientific innovation

  Proceedings of the National Academy of Sciences · 2022 · 150 citations

  Senior authorCorresponding

  With teams growing in all areas of scientific and scholarly research, we explore the relationship between team structure and the character of knowledge they produce. Drawing on 89,575 self-reports of team member research activity underlying scientific publications, we show how individual activities cohere into broad roles of 1) leadership through the direction and presentation of research and 2) support through data collection, analysis, and discussion. The hidden hierarchy of a scientific team…

  DOI

• Mobilizing Conceptual Spaces: How Word Embedding Models Can Inform Measurement and Theory Within Organization Science

  Organization Science · 2023 · 47 citations

  Senior authorCorresponding

  Word embedding models are a powerful approach for representing the multidimensional conceptual spaces within which communicated concepts relate, combine, and compete with one another. This class of models represent a recent advance in machine learning allowing scholars to efficiently encode complex systems of meaning with minimal semantic distortion based on local and global word co-occurrences from large-scale text data. Although their use has the potential to broaden theoretical possibilities…

  PublisherDOI

• Using human mobility data to quantify experienced urban inequalities

  Nature Human Behaviour · 2025-02-17 · 38 citations

  reviewSenior author
  PublisherDOI

• Human languages with greater information density have higher communication speed but lower conversation breadth

  Nature Human Behaviour · 2024 · 19 citations

  Senior authorCorresponding
  PublisherDOI

Recent grants

• Tracing Influence & Predicting Impact in Science

  NSF · $507k · 2013–2017

• Collaborative Research: Scaling Insight into Science: Assessing the value and effectiveness of machine assisted classification within a statistical system

  NSF · $196k · 2014–2017

• A Knowledge Base for Clinically Relevant Genes and Variants

  NIH · $9.0M · 2013–2018

Frequent coauthors

• Andrey Rzhetsky

  University of Chicago

  36 shared

• Jacob G. Foster

  23 shared

• Nati Srebro

  18 shared

• JOE M. PHILLIPS

  Emory University

  16 shared

• Nandana Sengupta

  Indian Institute of Technology Delhi

  16 shared

• Lingfei Wu

  University of Pittsburgh

  14 shared

• Dashun Wang

  Northwestern University

  11 shared

• Ishanu Chattopadhyay

  10 shared

Similar researchers at University of Chicago

• Lorraine Dastonh-58
• James T. Sparrowh-54
• Karin Knorr Cetinah-39