What leads managers to make inaccurate judgments?
When are they willing to use algorithms to improve their accuracy?
Jenn Logg
I am a Post-Doctoral Fellow at Harvard University. I received my Ph.D. from the Management of Organizations department at the University of California, Berkeley’s Haas School of Business.
My work examines why managers fail to view themselves and their work realistically. My research focuses on how people can assess themselves and the world more accurately by using advice and feedback produced by algorithms (scripts for mathematical calculations).
Updates:
- Invited revision at Journal of Experimental Psychology: General: Social contagion of overconfidence (Dec 2018)
- Published in Organizational Behavior and Human Decision Processes: Algorithm Appreciation (Job Market Paper)
- Published in Organizational Behavior and Human Decision Processes: Seeker Beware
- Published in Journal of Experimental Psychology: General: Is overconfidence a motivated bias?
- 2018 Early Career Award (as judged by editors of the five sections of JEP)
Upcoming Talk:
Conference on The Intelligence of Things
April 5, 2019
Washington, D.C.
Invited Talk: Algorithm Appreciation
Conference on The Intelligence of Things
April 5, 2019
Washington, D.C.
Invited Talk: Algorithm Appreciation
|
Oct. 26, 2018
Stop Naming Your Algorithms: Do People Trust Algorithms More Than Companies Realize? -Harvard Business Review |
I call my primary line of research, Theory of Machine. In it, I use 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. In a paper called, "Algorithm Appreciation," I examine when people are most likely to leverage the power of algorithmic advice to improve the accuracy of their judgments about the world. In a project on "Robo-Coaching," I test when people prefer performance feedback from an algorithm or from a person.
My second line of research investigates overconfidence, a pervasive bias. My work offers new insights into both the antecedents and consequences of these overly positive self-views. I test the questions: does valuing a particular skill increases people's overconfidence in that skill, is overconfidence socially "contagious," does optimism help performance as much as people expect, and does excessive confidence about winning an argument explain why everyone argues but no one is persuaded?
My second line of research investigates overconfidence, a pervasive bias. My work offers new insights into both the antecedents and consequences of these overly positive self-views. I test the questions: does valuing a particular skill increases people's overconfidence in that skill, is overconfidence socially "contagious," does optimism help performance as much as people expect, and does excessive confidence about winning an argument explain why everyone argues but no one is persuaded?
