Jennifer Logg

Jennifer M. Logg, Ph.D., is an Assistant Professor of Management at Georgetown University's McDonough School of Business. Prior to joining Georgetown, she was a Post-Doctoral Fellow at Harvard University. Dr. Logg received her Ph.D. from the University of California, Berkeley’s Haas School of Business.

Her work examines why people fail to view themselves and their work realistically. Her research focuses on how individuals can assess themselves and the world more accurately by using advice and feedback produced by algorithms (scripts for mathematical calculations).

She has been invited to speak on algorithms with decision-makers in the U.S. Senate, Air Force, and Navy. During her Ph.D., she was a collaborator on the Good Judgment Project, funded by IARPA, Intelligence of Advanced Research Projects Activity, the US intelligence community’s equivalent of DARPA.

In her primary line of research, Theory of Machine, she uses a psychological perspective to examine how people respond to the increasing prevalence of information produced by algorithms. Broadly, this work examines how people expect algorithmic and human judgment to differ. She recently wrote a book chapter on the topic, called The Psychology of Big Data: Developing a “Theory of Machine” to Examine Perceptions of Algorithms.

In 2021, her paper, "Algorithm Appreciation," ranked #1 on the list of "Most Cited Organizational Behavior and Human Decision Processes Articles Since 2018."

She received the 2019 Early Career Award for the paper "Is overconfidence a motivated bias?" as judged by the Journal of Experimental Psychology's editors (from five sections).